Image and video coding using machine learning prediction coding models

ABSTRACT

Video coding may include generating, by a processor, a decoded frame by decoding a current frame from an encoded bitstream and outputting a reconstructed frame based on the decoded frame. Decoding includes identifying a current encoded block from the current frame, identifying a prediction coding model for the current block, wherein the prediction coding model is a machine learning prediction coding model from a plurality of machine learning prediction coding models, identifying reference values for decoding the current block based on the prediction coding model, obtaining prediction values based on the prediction coding model and the reference values, generating a decoded block corresponding to the current encoded block based on the prediction values, and including the decoded block in the decoded frame.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. Application Pat. Serial No.16/295,176, filed Mar. 07, 2019, which claims priority to and thebenefit of U.S. Provisional Application Pat. Serial No. 62/778,271,filed Dec. 11, 2018, the entire disclosure of which is herebyincorporated by reference.

BACKGROUND

Digital images and video can be used, for example, on the internet, forremote business meetings via video conferencing, high definition videoentertainment, video advertisements, or sharing of user-generatedcontent. Due to the large amount of data involved in transferring andprocessing image and video data, high-performance compression may beadvantageous for transmission and storage. Accordingly, it would beadvantageous to provide high-resolution image and video transmitted overcommunications channels having limited bandwidth, such as image andvideo coding using machine learning prediction coding models.

SUMMARY

This application relates to encoding and decoding of image data, videostream data, or both for transmission or storage. Disclosed herein areaspects of systems, methods, and apparatuses for encoding and decodingusing machine learning prediction coding models.

An aspect is a method for video decoding comprising generating, by aprocessor, a decoded frame by decoding a current frame from an encodedbitstream and outputting a reconstructed frame based on the decodedframe. Decoding includes identifying a current encoded block from thecurrent frame, identifying a prediction coding model for the currentblock, wherein the prediction coding model is a machine learningprediction coding model from a plurality of machine learning predictioncoding models, identifying reference values for decoding the currentblock based on the prediction coding model, obtaining prediction valuesbased on the prediction coding model and the reference values,generating a decoded block corresponding to the current encoded blockbased on the prediction values, and including the decoded block in thedecoded frame.

Another aspect is an apparatus for video decoding, the apparatuscomprising a processor configured to generate a decoded frame bydecoding a current frame from an encoded bitstream. The decodingincludes identifying a current encoded block from the current frame,identifying a prediction coding model for the current block, wherein theprediction coding model is a machine learning prediction coding modelfrom a plurality of machine learning prediction coding models,identifying reference values for decoding the current block based on theprediction coding model, obtaining prediction values based on theprediction coding model and the reference values, generating a decodedblock corresponding to the current encoded block based on the predictionvalues, and including the decoded block in the decoded frame. Theprocessor is configured to output a reconstructed frame based on thedecoded frame.

Another aspect is a method for video decoding comprising generating, bya processor, a decoded frame by decoding a current frame from an encodedbitstream. The decoding includes identifying a current encoded blockfrom the current frame, decoding a prediction coding model identifierform the encoded bitstream, identifying a prediction coding model forthe current block based on the prediction coding model identifier,wherein the prediction coding model is a machine learning predictioncoding model from a plurality of machine learning prediction codingmodels, identifying reference values for decoding the current blockbased on the prediction coding model, obtaining prediction values basedon the prediction coding model and the reference values by using thereference values as input values for an artificial neural networkcorresponding to the prediction coding model such that the predictionvalues are output by the artificial neural network in response to thereference values, generating a decoded block corresponding to thecurrent encoded block based on the prediction values, and including thedecoded block in the decoded frame. The method includes outputting areconstructed frame based on the decoded frame.

Another aspect is an apparatus for video decoding, the apparatuscomprising a processor configured to generate a decoded frame bydecoding a current frame from an encoded bitstream. The decodingincludes identifying a current encoded block from the current frame,decoding a prediction coding model identifier form the encodedbitstream, identifying a prediction coding model for the current blockbased on the prediction coding model identifier, wherein the predictioncoding model is a machine learning prediction coding model from aplurality of machine learning prediction coding models, identifyingreference values for decoding the current block based on the predictioncoding model, obtaining prediction values based on the prediction codingmodel and the reference values by using the reference values as inputvalues for an artificial neural network corresponding to the predictioncoding model such that the prediction values are output by theartificial neural network in response to the reference values,generating a decoded block corresponding to the current encoded blockbased on the prediction values, and including the decoded block in thedecoded frame. The processor is configured to output a reconstructedframe based on the decoded frame.

Variations in these and other aspects will be described in additionaldetail hereafter.

BRIEF DESCRIPTION OF THE DRAWINGS

The description herein makes reference to the accompanying drawingswherein like reference numerals refer to like parts throughout theseveral views unless otherwise noted or otherwise clear from context.

FIG. 1 is a diagram of a computing device in accordance withimplementations of this disclosure.

FIG. 2 is a diagram of a computing and communications system inaccordance with implementations of this disclosure.

FIG. 3 is a diagram of a video stream for use in encoding and decodingin accordance with implementations of this disclosure.

FIG. 4 is a block diagram of an encoder in accordance withimplementations of this disclosure.

FIG. 5 is a block diagram of a decoder in accordance withimplementations of this disclosure.

FIG. 6 is a block diagram of a representation of a portion of a frame inaccordance with implementations of this disclosure.

FIG. 7 is a flowchart diagram of an example of encoding using machinelearning prediction coding models in accordance with implementations ofthis disclosure.

FIG. 8 is a flowchart diagram of an example of decoding using machinelearning prediction coding models in accordance with implementations ofthis disclosure.

FIG. 9 is a diagram of an example of ad-hoc DC intra-prediction inaccordance with implementations of this disclosure.

FIG. 10 is a diagram of an example of ad-hoc TrueMotion intra-predictionin accordance with implementations of this disclosure.

FIG. 11 is a diagram of an example of ad-hoc vertical intra-predictionin accordance with implementations of this disclosure.

FIG. 12 is a diagram of an example of ad-hoc horizontal intra-predictionin accordance with implementations of this disclosure.

FIG. 13 is a diagram of an example of ad-hoc diagonal down-rightintra-prediction in accordance with implementations of this disclosure.

FIG. 14 is a diagram of an example of ad-hoc diagonal down-leftintra-prediction in accordance with implementations of this disclosure.

FIG. 15 is a diagram of an example of ad-hoc horizontal-down-rightintra-prediction in accordance with implementations of this disclosure.

FIG. 16 is a diagram of an example of ad-hoc vertical-down-rightintra-prediction in accordance with implementations of this disclosure.

FIG. 17 is a diagram of an example of ad-hoc vertical-down-leftintra-prediction in accordance with implementations of this disclosure.

FIG. 18 is a diagram of an example of ad-hoc horizontal-up-rightintra-prediction in accordance with implementations of this disclosure.

FIG. 19 is a flowchart diagram of an example of machine learningprediction coding models in accordance with implementations of thisdisclosure.

DETAILED DESCRIPTION

Image and video compression schemes may include breaking an image, orframe, into smaller portions, such as blocks, and generating an outputbitstream using techniques to limit the information included for eachblock in the output. In some implementations, the information includedfor each block in the output may be limited by reducing spatialredundancy, reducing temporal redundancy, or a combination thereof. Forexample, temporal or spatial redundancies may be reduced by predicting aframe, or a portion thereof, based on information available to both theencoder and decoder, and including information representing adifference, or residual, between the predicted frame and the originalframe in the encoded bitstream. The residual information may be furthercompressed by transforming the residual information into transformcoefficients, quantizing the transform coefficients, and entropy codingthe quantized transform coefficients. Other coding information, such asmotion information, may be included in the encoded bitstream, which mayinclude transmitting differential information based on predictions ofthe encoding information, which may be entropy coded to further reducethe corresponding bandwidth utilization. An encoded bitstream can bedecoded to recreate the blocks and the source images from the limitedinformation. Prediction coding may include using ad-hoc predictioncoding models, which may inaccurately predict the input data, which maylimit coding efficiency.

Video coding using machine learning prediction coding models may improvethe accuracy and efficiency of prediction coding by using automaticallyoptimized prediction coding models, such as trained artificial neuralnetwork models in addition to, or instead of, the ad-hoc predictioncoding models. Using machine learning prediction coding models mayinclude training, or automatically optimizing, the machine learningprediction coding models by iteratively partitioning training data intotraining classes corresponding to the machine learning prediction codingmodels and training each machine learning prediction coding model basedon a respective partition of the training data such that definedconvergence criteria are satisfied.

FIG. 1 is a diagram of a computing device 100 in accordance withimplementations of this disclosure. The computing device 100 shownincludes a memory 110, a processor 120, a user interface (UI) 130, anelectronic communication unit 140, a sensor 150, a power source 160, anda bus 170. As used herein, the term “computing device” includes anyunit, or a combination of units, capable of performing any method, orany portion or portions thereof, disclosed herein.

The computing device 100 may be a stationary computing device, such as apersonal computer (PC), a server, a workstation, a minicomputer, or amainframe computer; or a mobile computing device, such as a mobiletelephone, a personal digital assistant (PDA), a laptop, or a tablet PC.Although shown as a single unit, any one element or elements of thecomputing device 100 can be integrated into any number of separatephysical units. For example, the user interface 130 and processor 120can be integrated in a first physical unit and the memory 110 can beintegrated in a second physical unit.

The memory 110 can include any non-transitory computer-usable orcomputer-readable medium, such as any tangible device that can, forexample, contain, store, communicate, or transport data 112,instructions 114, an operating system 116, or any information associatedtherewith, for use by or in connection with other components of thecomputing device 100. The non-transitory computer-usable orcomputer-readable medium can be, for example, a solid state drive, amemory card, removable media, a read-only memory (ROM), a random-accessmemory (RAM), any type of disk including a hard disk, a floppy disk, anoptical disk, a magnetic or optical card, an application-specificintegrated circuits (ASICs), or any type of non-transitory mediasuitable for storing electronic information, or any combination thereof.

Although shown a single unit, the memory 110 may include multiplephysical units, such as one or more primary memory units, such asrandom-access memory units, one or more secondary data storage units,such as disks, or a combination thereof. For example, the data 112, or aportion thereof, the instructions 114, or a portion thereof, or both,may be stored in a secondary storage unit and may be loaded or otherwisetransferred to a primary storage unit in conjunction with processing therespective data 112, executing the respective instructions 114, or both.In some implementations, the memory 110, or a portion thereof, may beremovable memory.

The data 112 can include information, such as input audio data, encodedaudio data, decoded audio data, or the like. The instructions 114 caninclude directions, such as code, for performing any method, or anyportion or portions thereof, disclosed herein. The instructions 114 canbe realized in hardware, software, or any combination thereof. Forexample, the instructions 114 may be implemented as information storedin the memory 110, such as a computer program, that may be executed bythe processor 120 to perform any of the respective methods, algorithms,aspects, or combinations thereof, as described herein.

Although shown as included in the memory 110, in some implementations,the instructions 114, or a portion thereof, may be implemented as aspecial purpose processor, or circuitry, that can include specializedhardware for carrying out any of the methods, algorithms, aspects, orcombinations thereof, as described herein. Portions of the instructions114 can be distributed across multiple processors on the same machine ordifferent machines or across a network such as a local area network, awide area network, the Internet, or a combination thereof.

The processor 120 can include any device or system capable ofmanipulating or processing a digital signal or other electronicinformation now-existing or hereafter developed, including opticalprocessors, quantum processors, molecular processors, or a combinationthereof. For example, the processor 120 can include a special purposeprocessor, a central processing unit (CPU), a digital signal processor(DSP), a plurality of microprocessors, one or more microprocessor inassociation with a DSP core, a controller, a microcontroller, anApplication Specific Integrated Circuit (ASIC), a Field ProgrammableGate Array (FPGA), a programmable logic array, programmable logiccontroller, microcode, firmware, any type of integrated circuit (IC), astate machine, or any combination thereof. As used herein, the term“processor” includes a single processor or multiple processors.

The user interface 130 can include any unit capable of interfacing witha user, such as a virtual or physical keypad, a touchpad, a display, atouch display, a speaker, a microphone, a video camera, a sensor, or anycombination thereof. For example, the user interface 130 may be anaudio-visual display device, and the computing device 100 may presentaudio, such as decoded audio, using the user interface 130 audio-visualdisplay device, such as in conjunction with displaying video, such asdecoded video. Although shown as a single unit, the user interface 130may include one or more physical units. For example, the user interface130 may include an audio interface for performing audio communicationwith a user, and a touch display for performing visual and touch-basedcommunication with the user.

The electronic communication unit 140 can transmit, receive, or transmitand receive signals via a wired or wireless electronic communicationmedium 180, such as a radio frequency (RF) communication medium, anultraviolet (UV) communication medium, a visible light communicationmedium, a fiber optic communication medium, a wireline communicationmedium, or a combination thereof. For example, as shown, the electroniccommunication unit 140 is operatively connected to an electroniccommunication interface 142, such as an antenna, configured tocommunicate via wireless signals.

Although the electronic communication interface 142 is shown as awireless antenna in FIG. 1 , the electronic communication interface 142can be a wireless antenna, as shown, a wired communication port, such asan Ethernet port, an infrared port, a serial port, or any other wired orwireless unit capable of interfacing with a wired or wireless electroniccommunication medium 180. Although FIG. 1 shows a single electroniccommunication unit 140 and a single electronic communication interface142, any number of electronic communication units and any number ofelectronic communication interfaces can be used.

The sensor 150 may include, for example, an audio-sensing device, avisible light-sensing device, a motion sensing device, or a combinationthereof. For example, 100the sensor 150 may include a sound-sensingdevice, such as a microphone, or any other sound-sensing device nowexisting or hereafter developed that can sense sounds in the proximityof the computing device 100, such as speech or other utterances, made bya user operating the computing device 100. In another example, thesensor 150 may include a camera, or any other image-sensing device nowexisting or hereafter developed that can sense an image such as theimage of a user operating the computing device. Although a single sensor150 is shown, the computing device 100 may include a number of sensors150. For example, the computing device 100 may include a first cameraoriented with a field of view directed toward a user of the computingdevice 100 and a second camera oriented with a field of view directedaway from the user of the computing device 100.

The power source 160 can be any suitable device for powering thecomputing device 100. For example, the power source 160 can include awired external power source interface; one or more dry cell batteries,such as nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride(NiMH), lithium-ion (Li-ion); solar cells; fuel cells; or any otherdevice capable of powering the computing device 100. Although a singlepower source 160 is shown in FIG. 1 , the computing device 100 mayinclude multiple power sources 160, such as a battery and a wiredexternal power source interface.

Although shown as separate units, the electronic communication unit 140,the electronic communication interface 142, the user interface 130, thepower source 160, or portions thereof, may be configured as a combinedunit. For example, the electronic communication unit 140, the electroniccommunication interface 142, the user interface 130, and the powersource 160 may be implemented as a communications port capable ofinterfacing with an external display device, providing communications,power, or both.

One or more of the memory 110, the processor 120, the user interface130, the electronic communication unit 140, the sensor 150, or the powersource 160, may be operatively coupled via a bus 170. Although a singlebus 170 is shown in FIG. 1 , a computing device 100 may include multiplebuses. For example, the memory 110, the processor 120, the userinterface 130, the electronic communication unit 140, the sensor 150,and the bus 170 may receive power from the power source 160 via the bus170. In another example, the memory 110, the processor 120, the userinterface 130, the electronic communication unit 140, the sensor 150,the power source 160, or a combination thereof, may communicate data,such as by sending and receiving electronic signals, via the bus 170.

Although not shown separately in FIG. 1 , one or more of the processor120, the user interface 130, the electronic communication unit 140, thesensor 150, or the power source 160 may include internal memory, such asan internal buffer or register. For example, the processor 120 mayinclude internal memory (not shown) and may read data 112 from thememory 110 into the internal memory (not shown) for processing.

Although shown as separate elements, the memory 110, the processor 120,the user interface 130, the electronic communication unit 140, thesensor 150, the power source 160, and the bus 170, or any combinationthereof can be integrated in one or more electronic units, circuits, orchips.

FIG. 2 is a diagram of a computing and communications system 200 inaccordance with implementations of this disclosure. The computing andcommunications system 200 shown includes computing and communicationdevices 100A, 100B, 100C, access points 210A, 210B, and a network 220.For example, the computing and communication system 200 can be amultiple access system that provides communication, such as voice,audio, data, video, messaging, broadcast, or a combination thereof, toone or more wired or wireless communicating devices, such as thecomputing and communication devices 100A, 100B, 100C. Although, forsimplicity, FIG. 2 shows three computing and communication devices 100A,100B, 100C, two access points 210A, 210B, and one network 220, anynumber of computing and communication devices, access points, andnetworks can be used.

A computing and communication device 100A, 100B, 100C can be, forexample, a computing device, such as the computing device 100 shown inFIG. 1 . For example, the computing and communication devices 100A, 100Bmay be user devices, such as a mobile computing device, a laptop, a thinclient, or a smartphone, and the computing and communication device 100Cmay be a server, such as a mainframe or a cluster. Although thecomputing and communication device 100A and the computing andcommunication device 100B are described as user devices, and thecomputing and communication device 100C is described as a server, anycomputing and communication device may perform some or all of thefunctions of a server, some or all of the functions of a user device, orsome or all of the functions of a server and a user device. For example,the server computing and communication device 100C may receive, encode,process, store, transmit, or a combination thereof audio data and one orboth of the computing and communication device 100A and the computingand communication device 100B may receive, decode, process, store,present, or a combination thereof the audio data.

Each computing and communication device 100A, 100B, 100C, which mayinclude a user equipment (UE), a mobile station, a fixed or mobilesubscriber unit, a cellular telephone, a personal computer, a tabletcomputer, a server, consumer electronics, or any similar device, can beconfigured to perform wired or wireless communication, such as via thenetwork 220. For example, the computing and communication devices 100A,100B, 100C can be configured to transmit or receive wired or wirelesscommunication signals. Although each computing and communication device100A, 100B, 100C is shown as a single unit, a computing andcommunication device can include any number of interconnected elements.

Each access point 210A, 210B can be any type of device configured tocommunicate with a computing and communication device 100A, 100B, 100C,a network 220, or both via wired or wireless communication links 180A,180B, 180C. For example, an access point 210A, 210B can include a basestation, a base transceiver station (BTS), a Node-B, an enhanced Node-B(eNode-B), a Home Node-B (HNode-B), a wireless router, a wired router, ahub, a relay, a switch, or any similar wired or wireless device.Although each access point 210A, 210B is shown as a single unit, anaccess point can include any number of interconnected elements.

The network 220 can be any type of network configured to provideservices, such as voice, data, applications, voice over internetprotocol (VoIP), or any other communications protocol or combination ofcommunications protocols, over a wired or wireless communication link.For example, the network 220 can be a local area network (LAN), widearea network (WAN), virtual private network (VPN), a mobile or cellulartelephone network, the Internet, or any other means of electroniccommunication. The network can use a communication protocol, such as thetransmission control protocol (TCP), the user datagram protocol (UDP),the internet protocol (IP), the real-time transport protocol (RTP) theHyperText Transport Protocol (HTTP), or a combination thereof.

The computing and communication devices 100A, 100B, 100C can communicatewith each other via the network 220 using one or more a wired orwireless communication links, or via a combination of wired and wirelesscommunication links. For example, as shown the computing andcommunication devices 100A, 100B can communicate via wirelesscommunication links 180A, 180B, and computing and communication device100C can communicate via a wired communication link 180C. Any of thecomputing and communication devices 100A, 100B, 100C may communicateusing any wired or wireless communication link, or links. For example, afirst computing and communication device 100A can communicate via afirst access point 210A using a first type of communication link, asecond computing and communication device 100B can communicate via asecond access point 210B using a second type of communication link, anda third computing and communication device 100C can communicate via athird access point (not shown) using a third type of communication link.Similarly, the access points 210A, 210B can communicate with the network220 via one or more types of wired or wireless communication links 230A,230B. Although FIG. 2 shows the computing and communication devices100A, 100B, 100C in communication via the network 220, the computing andcommunication devices 100A, 100B, 100C can communicate with each othervia any number of communication links, such as a direct wired orwireless communication link.

In some implementations, communications between one or more of thecomputing and communication device 100A, 100B, 100C may omitcommunicating via the network 220 and may include transferring data viaanother medium (not shown), such as a data storage device. For example,the server computing and communication device 100C may store audio data,such as encoded audio data, in a data storage device, such as a portabledata storage unit, and one or both of the computing and communicationdevice 100 A or the computing and communication device 100B may access,read, or retrieve the stored audio data from the data storage unit, suchas by physically disconnecting the data storage device from the servercomputing and communication device 100C and physically connecting thedata storage device to the computing and communication device 100A orthe computing and communication device 100B.

Other implementations of the computing and communications system 200 arepossible. For example, in an implementation, the network 220 can be anad-hoc network and can omit one or more of the access points 210A, 210B.The computing and communications system 200 may include devices, units,or elements not shown in FIG. 2 . For example, the computing andcommunications system 200 may include many more communicating devices,networks, and access points.

FIG. 3 is a diagram of a video stream 300 for use in encoding anddecoding in accordance with implementations of this disclosure. A videostream 300, such as a video stream captured by a video camera or a videostream generated by a computing device, may include a video sequence310. The video sequence 310 may include a sequence of adjacent frames320. Although three adjacent frames 320 are shown, the video sequence310 can include any number of adjacent frames 320.

Each frame 330 from the adjacent frames 320 may represent a single imagefrom the video stream. Although not shown in FIG. 3 , a frame 330 mayinclude one or more segments, tiles, or planes, which may be coded, orotherwise processed, independently, such as in parallel. A frame 330 mayinclude blocks 340. Although not shown in FIG. 3 , a block can includepixels. For example, a block can include a 16_(X) 16 group of pixels, an₈×₈ group of pixels, an 8×16 group of pixels, or any other group ofpixels. Unless otherwise indicated herein, the term ‘block’ can includea superblock, a macroblock, a segment, a slice, or any other portion ofa frame. A frame, a block, a pixel, or a combination thereof can includedisplay information, such as luminance information, chrominanceinformation, or any other information that can be used to store, modify,communicate, or display the video stream or a portion thereof.

FIG. 4 is a block diagram of an encoder 400 in accordance withimplementations of this disclosure. Encoder 400 can be implemented in adevice, such as the computing device 100 shown in FIG. 1 or thecomputing and communication devices 100A, 100B, 100C shown in FIG. 2 ,as, for example, a computer software program stored in a data storageunit, such as the memory 110 shown in FIG. 1 . The computer softwareprogram can include machine instructions that may be executed by aprocessor, such as the processor 120 shown in FIG. 1 , and may cause thedevice to encode video data as described herein. The encoder 400 can beimplemented as specialized hardware included, for example, in computingdevice 100.

The encoder 400 can encode an input video stream 402, such as the videostream 300 shown in FIG. 3 , to generate an encoded (compressed)bitstream 404. In some implementations, the encoder 400 may include aforward path for generating the compressed bitstream 404. The forwardpath may include an intra/inter prediction unit 410, a transform unit420, a quantization unit 430, an entropy encoding unit 440, or anycombination thereof. In some implementations, the encoder 400 mayinclude a reconstruction path (indicated by the broken connection lines)to reconstruct a frame for encoding of further blocks. Thereconstruction path may include a dequantization unit 450, an inversetransform unit 460, a reconstruction unit 470, a filtering unit 480, orany combination thereof. Other structural variations of the encoder 400can be used to encode the video stream 402.

For encoding the video stream 402, each frame within the video stream402 can be processed in units of blocks. Thus, a current block may beidentified from the blocks in a frame, and the current block may beencoded.

At the intra/inter prediction unit 410, the current block can be encodedusing either intra-frame prediction, which may be within a single frame,or inter-frame prediction, which may be from frame to frame.Intra-prediction may include generating a prediction block from samplesin the current frame that have been previously encoded andreconstructed. Inter-prediction may include generating a predictionblock from samples in one or more previously constructed referenceframes. Generating a prediction block for a current block in a currentframe may include performing motion estimation to generate a motionvector indicating an appropriate reference portion of the referenceframe.

The intra/inter prediction unit 410 may subtract the prediction blockfrom the current block (raw block) to produce a residual block. Thetransform unit 420 may perform a block-based transform, which mayinclude transforming the residual block into transform coefficients in,for example, the frequency domain. Examples of block-based transformsinclude the Karhunen-Loeve Transform (KLT), the Discrete CosineTransform (DCT), the Singular Value Decomposition Transform (SVD), andthe Asymmetric Discrete Sine Transform (ADST). In an example, the DCTmay include transforming a block into the frequency domain. The DCT mayinclude using transform coefficient values based on spatial frequency,with the lowest frequency (i.e. DC) coefficient at the top-left of thematrix and the highest frequency coefficient at the bottom-right of thematrix.

The quantization unit 430 may convert the transform coefficients intodiscrete quantum values, which may be referred to as quantized transformcoefficients or quantization levels. The quantized transformcoefficients can be entropy encoded by the entropy encoding unit 440 toproduce entropy-encoded coefficients. Entropy encoding can include usinga probability distribution metric. The entropy-encoded coefficients andinformation used to decode the block, which may include the type ofprediction used, motion vectors, and quantizer values, can be output tothe compressed bitstream 404. The compressed bitstream 404 can beformatted using various techniques, such as run-length encoding (RLE)and zero-run coding.

The reconstruction path can be used to maintain reference framesynchronization between the encoder 400 and a corresponding decoder,such as the decoder 500 shown in FIG. 5 . The reconstruction path may besimilar to the decoding process discussed below and may include decodingthe encoded frame, or a portion thereof, which may include decoding anencoded block, which may include dequantizing the quantized transformcoefficients at the dequantization unit 450 and inverse transforming thedequantized transform coefficients at the inverse transform unit 460 toproduce a derivative residual block. The reconstruction unit 470 may addthe prediction block generated by the intra/inter prediction unit 410 tothe derivative residual block to create a decoded block. The filteringunit 480 can be applied to the decoded block to generate a reconstructedblock, which may reduce distortion, such as blocking artifacts. Althoughone filtering unit 480 is shown in FIG. 4 , filtering the decoded blockmay include loop filtering, deblocking filtering, or other types offiltering or combinations of types of filtering. The reconstructed blockmay be stored or otherwise made accessible as a reconstructed block,which may be a portion of a reference frame, for encoding anotherportion of the current frame, another frame, or both, as indicated bythe broken line at 482. Coding information, such as deblocking thresholdindex values, for the frame may be encoded, included in the compressedbitstream 404, or both, as indicated by the broken line at 484.

Other variations of the encoder 400 can be used to encode the compressedbitstream 404. For example, a non-transform-based encoder 400 canquantize the residual block directly without the transform unit 420. Insome implementations, the quantization unit 430 and the dequantizationunit 450 may be combined into a single unit.

FIG. 5 is a block diagram of a decoder 500 in accordance withimplementations of this disclosure. The decoder 500 can be implementedin a device, such as the computing device 100 shown in FIG. 1 or thecomputing and communication devices 100A, 100B, 100C shown in FIG. 2 ,as, for example, a computer software program stored in a data storageunit, such as the memory 110 shown in FIG. 1 . The computer softwareprogram can include machine instructions that may be executed by aprocessor, such as the processor 120 shown in FIG. 1 , and may cause thedevice to decode video data as described herein. The decoder 500 can beimplemented as specialized hardware included, for example, in computingdevice 100.

The decoder 500 may receive a compressed bitstream 502, such as thecompressed bitstream 404 shown in FIG. 4 , and may decode the compressedbitstream 502 to generate an output video stream 504. The decoder 500may include an entropy decoding unit 510, a dequantization unit 520, aninverse transform unit 530, an intra/inter prediction unit 540, areconstruction unit 550, a filtering unit 560, or any combinationthereof. Other structural variations of the decoder 500 can be used todecode the compressed bitstream 502.

The entropy decoding unit 510 may decode data elements within thecompressed bitstream 502 using, for example, Context Adaptive BinaryArithmetic Decoding, to produce a set of quantized transformcoefficients. The dequantization unit 520 can dequantize the quantizedtransform coefficients, and the inverse transform unit 530 can inversetransform the dequantized transform coefficients to produce a derivativeresidual block, which may correspond to the derivative residual blockgenerated by the inverse transform unit 460 shown in FIG. 4 . Usingheader information decoded from the compressed bitstream 502, theintra/inter prediction unit 540 may generate a prediction blockcorresponding to the prediction block created in the encoder 400. At thereconstruction unit 550, the prediction block can be added to thederivative residual block to create a decoded block. The filtering unit560 can be applied to the decoded block to reduce artifacts, such asblocking artifacts, which may include loop filtering, deblockingfiltering, or other types of filtering or combinations of types offiltering, and which may include generating a reconstructed block, whichmay be output as the output video stream 504.

Other variations of the decoder 500 can be used to decode the compressedbitstream 502. For example, the decoder 500 can produce the output videostream 504 without the deblocking filtering unit 570.

FIG. 6 is a block diagram of a representation of a portion 600 of aframe, such as the frame 330 shown in FIG. 3 , in accordance withimplementations of this disclosure. As shown, the portion 600 of theframe includes four 64X64 blocks 610, in two rows and two columns in amatrix or Cartesian plane. In some implementations, a 64X64 block may bea maximum coding unit, N=64. Each 64×64 block may include four 32x32blocks 620. Each 32x32 block may include four 16X16 blocks 630. Each 16X16 block may include four 8×8 blocks 640. Each 8×8 block 640 may includefour 4×4 blocks 650. Each 4×4 block 650 may include 16 pixels, which maybe represented in four rows and four columns in each respective block inthe Cartesian plane or matrix. The pixels may include informationrepresenting an image captured in the frame, such as luminanceinformation, color information, and location information. In someimplementations, a block, such as a 16X16 pixel block as shown, mayinclude a luminance block 660, which may include luminance pixels 662;and two chrominance blocks 670, 680, such as a U or Cb chrominance block670, and a V or Cr chrominance block 680. The chrominance blocks 670,680 may include chrominance pixels 690. For example, the luminance block660 may include 16 × 16 luminance pixels 662 and each chrominance block670, 680 may include 8×8 chrominance pixels 690 as shown. Although onearrangement of blocks is shown, any arrangement may be used. AlthoughFIG. 6 shows N×N blocks, in some implementations, N×M blocks may beused. For example, 32×64 blocks, 64×32 blocks, 16×32 blocks, 32×16blocks, or any other size blocks may be used. In some implementations,N×2N blocks, 2N×N blocks, or a combination thereof may be used.

In some implementations, video coding may include ordered block-levelcoding. Ordered block-level coding may include coding blocks of a framein an order, such as raster-scan order, wherein blocks may be identifiedand processed starting with a block in the upper left corner of theframe, or portion of the frame, and proceeding along rows from left toright and from the top row to the bottom row, identifying each block inturn for processing. For example, the 64×64 block in the top row andleft column of a frame may be the first block coded and the 64×64 blockimmediately to the right of the first block may be the second blockcoded. The second row from the top may be the second row coded, suchthat the 64X64 block in the left column of the second row may be codedafter the 64X64 block in the rightmost column of the first row.

In some implementations, coding a block may include using quad-treecoding, which may include coding smaller block units within a block inraster-scan order. For example, the 64×64 block shown in the bottom leftcorner of the portion of the frame shown in FIG. 6 , may be coded usingquad-tree coding wherein the top left 32x32 block may be coded, then thetop right 32X32 block may be coded, then the bottom left 32X32 block maybe coded, and then the bottom right 32X32 block may be coded. Each 32×32block may be coded using quad-tree coding wherein the top left 16 × 16block may be coded, then the top right 16 × 16 block may be coded, thenthe bottom left 16 × 16 block may be coded, and then the bottom right 16× 16 block may be coded. Each 16X 16 block may be coded using quad-treecoding wherein the top left 8×8 block may be coded, then the top right8×8 block may be coded, then the bottom left 8×8 block may be coded, andthen the bottom right 8×8 block may be coded. Each 8×8 block may becoded using quad-tree coding wherein the top left 4×4 block may becoded, then the top right 4×4 block may be coded, then the bottom left4×4 block may be coded, and then the bottom right 4×4 block may becoded. In some implementations, 8×8 blocks may be omitted for a 16X16block, and the 16X16 block may be coded using quad-tree coding whereinthe top left 4×4 block may be coded, then the other 4×4 blocks in the16X16 block may be coded in raster-scan order.

In some implementations, video coding may include compressing theinformation included in an original, or input, frame by, for example,omitting some of the information in the original frame from acorresponding encoded frame. For example, coding may include reducingspectral redundancy, reducing spatial redundancy, reducing temporalredundancy, or a combination thereof.

In some implementations, reducing spectral redundancy may include usinga color model based on a luminance component (Y) and two chrominancecomponents (U and V or Cb and Cr), which may be referred to as the YUVor YCbCr color model, or color space. Using the YUV color model mayinclude using a relatively large amount of information to represent theluminance component of a portion of a frame and using a relatively smallamount of information to represent each corresponding chrominancecomponent for the portion of the frame. For example, a portion of aframe may be represented by a high-resolution luminance component, whichmay include a 16X16 block of pixels, and by two lower resolutionchrominance components, each of which represents the portion of theframe as an 8×8 block of pixels. A pixel may indicate a value, forexample, a value in the range from 0 to 255, and may be stored ortransmitted using, for example, eight bits. Although this disclosure isdescribed in reference to the YUV color model, any color model may beused.

In some implementations, reducing spatial redundancy may includetransforming a block into the frequency domain using, for example, adiscrete cosine transform (DCT). For example, a unit of an encoder, suchas the transform unit 420 shown in FIG. 4 , may perform a DCT usingtransform coefficient values based on spatial frequency.

In some implementations, reducing temporal redundancy may include usingsimilarities between frames to encode a frame using a relatively smallamount of data based on one or more reference frames, which may bepreviously encoded, decoded, and reconstructed frames of the videostream. For example, a block or pixel of a current frame may be similarto a spatially corresponding block or pixel of a reference frame. Insome implementations, a block or pixel of a current frame may be similarto block or pixel of a reference frame at a different spatial locationand reducing temporal redundancy may include generating motioninformation indicating the spatial difference, or translation, betweenthe location of the block or pixel in the current frame andcorresponding location of the block or pixel in the reference frame.

In some implementations, reducing temporal redundancy may includeidentifying a portion of a reference frame that corresponds to a currentblock or pixel of a current frame. For example, a reference frame, or aportion of a reference frame, which may be stored in memory, may besearched to identify a portion for generating a prediction to use forencoding a current block or pixel of the current frame with maximalefficiency. For example, the search may identify a portion of thereference frame for which the difference in pixel values between thecurrent block and a prediction block generated based on the portion ofthe reference frame is minimized and may be referred to as motionsearching. In some implementations, the portion of the reference framesearched may be limited. For example, the portion of the reference framesearched, which may be referred to as the search area, may include alimited number of rows of the reference frame. In an example,identifying the portion of the reference frame for generating aprediction may include calculating a cost function, such as a sum ofabsolute differences (SAD), between the pixels of portions of the searcharea and the pixels of the current block.

In some implementations, the spatial difference between the location ofthe portion of the reference frame for generating a prediction in thereference frame and the current block in the current frame may berepresented as a motion vector. The difference in pixel values betweenthe prediction block and the current block may be referred to asdifferential data, residual data, a prediction error, or as a residualblock. In some implementations, generating motion vectors may bereferred to as motion estimation, and a pixel of a current block may beindicated based on location using Cartesian coordinates as fX,y.Similarly, a pixel of the search area of the reference frame may beindicated based on location using Cartesian coordinates as rx,y_(.) Amotion vector (MV) for the current block may be determined based on, forexample, a SAD between the pixels of the current frame and thecorresponding pixels of the reference frame.

Although described herein with reference to matrix or Cartesianrepresentation of a frame for clarity, a frame may be stored,transmitted, processed, or any combination thereof, in any datastructure such that pixel values may be efficiently represented for aframe or image. For example, a frame may be stored, transmitted,processed, or any combination thereof, in a two-dimensional datastructure such as a matrix as shown, or in a one-dimensional datastructure, such as a vector array. In an implementation, arepresentation of the frame, such as a two-dimensional representation asshown, may correspond to a physical location in a rendering of the frameas an image. For example, a location in the top left corner of a blockin the top left corner of the frame may correspond with a physicallocation in the top left corner of a rendering of the frame as an image.

In some implementations, block-based coding efficiency may be improvedby partitioning input blocks into one or more prediction partitions,which may be rectangular, including square, partitions for predictioncoding. In some implementations, video coding using predictionpartitioning may include selecting a prediction partitioning scheme fromamong multiple candidate prediction partitioning schemes. For example,in some implementations, candidate prediction partitioning schemes for a64X64 coding unit may include rectangular size prediction partitionsranging in sizes from 4×4 to 64×64, such as 4×4, 4×8, 8×4, 8×8, 8× 16,16X8, 16x16, 16×32, 32×16, 32×32, 32×64, 64×32, or 64X64. In someimplementations, video coding using prediction partitioning may includea full prediction partition search, which may include selecting aprediction partitioning scheme by encoding the coding unit using eachavailable candidate prediction partitioning scheme and selecting thebest scheme, such as the scheme that produces the least rate-distortionerror.

In some implementations, encoding a video frame may include identifyinga prediction partitioning scheme for encoding a current block, such asblock 610. In some implementations, identifying a predictionpartitioning scheme may include determining whether to encode the blockas a single prediction partition of maximum coding unit size, which maybe 64X64 as shown, or to partition the block into multiple predictionpartitions, which may correspond with the sub-blocks, such as the 32×32blocks 620 the 16×16 blocks 630, or the 8x8 blocks 640, as shown, andmay include determining whether to partition into one or more smallerprediction partitions. For example, a 64×64 block may be partitionedinto four 32×32 prediction partitions. Three of the four 32×32prediction partitions may be encoded as 32X32 prediction partitions andthe fourth 32X32 prediction partition may be further partitioned intofour 16x16 prediction partitions. Three of the four 16x 16 predictionpartitions may be encoded as 16x16 prediction partitions and the fourth16X 16 prediction partition may be further partitioned into four 8×8prediction partitions, each of which may be encoded as an 8×8 predictionpartition. In some implementations, identifying the predictionpartitioning scheme may include using a prediction partitioning decisiontree.

In some implementations, video coding for a current block may includeidentifying an optimal prediction coding mode from multiple candidateprediction coding modes, which may provide flexibility in handling videosignals with various statistical properties and may improve thecompression efficiency. For example, a video coder may evaluate eachcandidate prediction coding mode to identify the optimal predictioncoding mode, which may be, for example, the prediction coding mode thatminimizes an error metric, such as a rate-distortion cost, for thecurrent block. In some implementations, the complexity of searching thecandidate prediction coding modes may be reduced by limiting the set ofavailable candidate prediction coding modes based on similaritiesbetween the current block and a corresponding prediction block. In someimplementations, the complexity of searching each candidate predictioncoding mode may be reduced by performing a directed refinement modesearch. For example, metrics may be generated for a limited set ofcandidate block sizes, such as 16×16, 8×8, and 4×4, the error metricassociated with each block size may be in descending order, andadditional candidate block sizes, such as 4×8 and 8×4 block sizes, maybe evaluated.

In some implementations, block-based coding efficiency may be improvedby partitioning a current residual block into one or more transformpartitions, which may be rectangular, including square, partitions fortransform coding. In some implementations, video coding using transformpartitioning may include selecting a uniform transform partitioningscheme. For example, a current residual block, such as block 610, may bea 64×64 block and may be transformed without partitioning using a 64X64transform.

Although not expressly shown in FIG. 6 , a residual block may betransform partitioned using a uniform transform partitioning scheme. Forexample, a 64X64 residual block may be transform partitioned using auniform transform partitioning scheme including four 32×32 transformblocks, using a uniform transform partitioning scheme including sixteen16×16 transform blocks, using a uniform transform partitioning schemeincluding sixty-four 8×8 transform blocks, or using a uniform transformpartitioning scheme including 256 4×4 transform blocks.

In some implementations, video coding using transform partitioning mayinclude identifying multiple transform block sizes for a residual blockusing multiform transform partition coding. In some implementations,multiform transform partition coding may include recursively determiningwhether to transform a current block using a current block sizetransform or by partitioning the current block and multiform transformpartition coding each partition. For example, the bottom left block 610shown in FIG. 6 may be a 64×64 residual block, and multiform transformpartition coding may include determining whether to code the current64×64 residual block using a 64×64 transform or to code the 64×64residual block by partitioning the 64×64 residual block into partitions,such as four 32×32 blocks 620, and multiform transform partition codingeach partition. In some implementations, determining whether totransform partition the current block may be based on comparing a costfor encoding the current block using a current block size transform to asum of costs for encoding each partition using partition sizetransforms.

FIG. 7 is a flowchart diagram of an example of encoding using machinelearning prediction coding models 700 in accordance with implementationsof this disclosure. Encoding using machine learning prediction codingmodels 700 may be implemented in an encoder, such as the encoder 400shown in FIG. 4 . For example, the intra/inter prediction unit 410 ofthe encoder 400 shown in FIG. 4 may implement encoding using machinelearning prediction coding models 700.

Encoding using machine learning prediction coding models 700 may includeidentifying a current block at 710, generating an encoded block byencoding the current block at 720, and outputting the encoded block at730.

The current block may be identified at 710. Identifying the currentblock may include identifying a current frame, such as an input frame,and identifying the current block from the current frame. For example,the current block may be a block, such as one of the blocks 610, 620,630, 640, 650 shown in FIG. 6 . The current block may be identifiedaccording to a block scan order. In some implementations, identifyingthe current block at 710 may include identifying a tile from the currentframe and identifying the current block from the tile. Although encodingusing machine learning prediction coding models 700 is described withreference to forward raster scan order, any block scan order may beused.

An encoded block may be generated at 720 by encoding the current blockidentified at 710. Encoding the current block at 720 may includegenerating a prediction block for the current block, which may includeidentifying a prediction model at 722, identifying reference pixelvalues at 724, and generating prediction values at 726.

Identifying the prediction model at 722 may include identifying aprediction mode, such as an intra-prediction coding mode, aninter-prediction coding mode, or a compound prediction coding mode, suchas an inter-inter mode or an inter-intra mode. Encoding using aninter-inter coding mode may include combining multiple inter-predictiongenerated predictions. Encoding using an inter-intra coding mode mayinclude combining inter-prediction generated predictions withintra-prediction generated predictions. Identifying the prediction modeat 722 may include identifying the prediction mode from a set ofcandidate prediction modes.

Identifying the prediction model at 722 may include identifying anautomatically optimized coding model, which may be a machine learning orartificial intelligence coding model, such as an artificial neuralnetwork prediction coding model. For example, an artificial neuralnetwork prediction coding model may receive, as input, reference values(x) for an input set {x, z}, which may correspond to a current block (z)and may output prediction values (z_(p)) for the current block (z) ofthe input set {x, z}. The artificial neural network model may implementa neural network functionƒnn() for generating the prediction values(z_(p)) corresponding to the input pixel values (p) for a current block(z) of the input set {x, z} based on the reference values (x), which maybe expressed as the following:

z_(p) = f_(nn)(x).

An artificial neural network model may describe nodes, or artificialneurons. A node in an artificial neural network may be expressed as amathematical function, which may include describing or defining one ormore parameters or thresholds for the node. A node in an artificialneural network may receive one or more input signals, determine aninternal state subsequent to, or in accordance with, receiving the inputsignals (activation), and output an output signal based on the inputsignals and the internal state. The input signals may be associated withrespective weighting values. The artificial neural network model maydescribe or define the weighting values. For example, determining theinternal state may include determining a weighted sum of the inputsignals, transforming the sum, such as using an activation or transformfunction, which may be a non-linear function, and outputting thetransformation result, or a function (output function) thereof.

The artificial neural network model may describe layers for organizingand arranging the nodes in the artificial neural network, such as aninput layer, an output layer, and zero or more intermediate, internal,or hidden layers. The nodes of the artificial neural network input layer(input nodes) may receive the artificial neural network input data, suchas the reference values (x). Nodes in adjacent layers may beinterconnected along edges. The artificial neural network model maydescribe or define weighting values associated with respective edges.The output nodes in the output layer of the artificial neural networkmay output prediction values based on the received input referencevalues. For example, for a 4×4 block the input layer may include adefined cardinality of input notes, such as nine input nodes, forreceiving the reference values (x), such as nine reference values, andthe output layer may include a defined cardinality of output nodes, suchas 16 output nodes corresponding to the 16 prediction values (z_(p)) forthe 16 pixels of the current block.

As an example, an artificial neural network intra-prediction codingmodel may be a fully connected artificial neural network including adefined cardinality, such as two or three, layers, which may include aninput layer having a cardinality of nodes, or artificial neurons,corresponding to the cardinality of the reference values (x), orfeatures, for an input set {x, z}.

Although encoding using an artificial neural network encoding mode modelis described herein with referenced to intra-prediction coding, encodingusing an artificial neural network encoding mode model may be used forother types of prediction coding. For example, an artificial neuralnetwork compound prediction coding model may include a convolutionalneural network, or a combination of a convolutional neural network and afully connected artificial neural network, wherein the input (x) may bedecomposed into respective reference data sets (x₁, x₂).

Encoding, or decoding, using an artificial neural network predictioncoding model may include instantiating or operating an instance of anartificial neural network described by the artificial neural networkmodel. Encoding using machine learning prediction coding models 700 mayinclude generating the artificial neural network models. An example ofgenerating artificial neural network models is shown in FIG. 19 .

The available reference values may be identified at 724. For example,the prediction model identified at 722 may be an intra-prediction modeland identifying the available reference values at 724 may includeidentifying the prediction values based on available reference valuesthat correspond to pixels spatially proximate to, such as adjacent to orneighboring, the current block from the current frame, such as values ofpreviously predicted, encoded, decoded, and reconstructed pixels. Inanother example, the prediction model identified at 722 may be aninter-prediction model and identifying the available reference values at724 may include identifying the prediction values based on availablereference values, such as values of previously predicted, encoded,decoded, and reconstructed pixels, determined based on a reference fameother than the current frame. In another example, the prediction modelidentified at 722 may be a compound inter-inter-prediction model andidentifying the available reference values at 724 may include generatingthe prediction values based on available reference values, such asvalues of previously predicted, encoded, decoded, and reconstructedpixels, determined based on multiple reference frames other than thecurrent frame. In another example, the prediction model identified at722 may be a compound inter-intra-prediction model and identifying theavailable reference values at 724 may include generating the predictionvalues, such as values of previously predicted, encoded, decoded, andreconstructed pixels, based on available reference values determinedbased on the current frame and based on a reference fame other than thecurrent frame.

Prediction values may be generated at 726 based on the prediction modelidentified at 722 and the available reference values identified at 724.For example, generating prediction values at 726 based on the artificialneural network prediction coding model identified at 722 and theavailable reference values identified at 724 may include determining aprediction value for each pixel of the current block by inputting theavailable reference values identified at 724 to the artificial neuralnetwork prediction coding model identified at 722 and obtaining theoutput of the artificial neural network prediction coding modelsidentified at 722 as the prediction values.

In some implementations, generating an encoded block at 720 by encodingthe current block identified at 710 may include evaluating multiplecandidate prediction modes as indicated by the broken line at 728.Evaluating multiple candidate prediction modes may include for eachcandidate prediction mode from the candidate prediction modes,identifying the candidate prediction mode at 722, identifying referencevalues at 724, generating prediction values at 726, and determining anefficiency metric based on the prediction values (not shown). Theefficiency metric may indicate, for example, a measure of predictionaccuracy, such as a sum of absolute differences (SAD), between theprediction values and the pixels of the current block. The candidateprediction mode corresponding to the minimal efficiency metric may beidentified as the prediction mode for the current block, and thecorresponding prediction block may be used as the prediction block.

Although not shown expressly in FIG. 7 , generating an encoded block byencoding the current encoded block at 720 may include performing otherelements of video encoding, such as transformation by a transform unit,such as the transform unit 420 shown in FIG. 4 , quantization by aquantization unit, such as the quantization unit 430 shown in FIG. 4 ,entropy coding by an entropy coding unit, such as the entropy codingunit 440 shown in FIG. 4 , dequantization by a dequantization unit, suchas the dequantization unit 450 shown in FIG. 4 , inverse transformationby an inverse transform unit, such as the inverse transform unit 460shown in FIG. 4 , reconstruction by a reconstruction unit, such as thereconstruction unit 470 shown in FIG. 4 , or any other aspect of videocoding.

Information identifying the prediction mode, the encoded block, or bothmay be output at 730. For example, a prediction mode indicator, such asan index value, corresponding to the prediction mode identified at 722may be included in an output bitstream, such as in a header for theblock.

Other implementations of encoding using machine learning predictioncoding models 700 are available. For example, other classes ofartificial neural networks may be used. In some implementations,additional elements of encoding using machine learning prediction codingmodels can be added, certain elements can be combined, and/or certainelements can be removed.

FIG. 8 is a flowchart diagram of an example of decoding using machinelearning prediction coding models 800 in accordance with implementationsof this disclosure. Decoding using machine learning prediction codingmodels 800 may be implemented in a decoder, such as the decoder 500shown in FIG. 5 . For example, the intra/inter prediction unit 540 ofthe decoder 500 shown in FIG. 5 may implement decoding using machinelearning prediction coding models 800.

Decoding using machine learning prediction coding models 800 may includeidentifying a current encoded block at 810, generating a decoded blockby decoding the current encoded block at 820, and outputting the decodedblock at 830.

The current encoded block may be identified at 810. Identifying thecurrent encoded block may include identifying a current encoded frameand identifying the current encoded block from the current encodedframe. For example, the current encoded block may be a block, such asone of the blocks 610, 620, 630, 640, 650 shown in FIG. 6 . For example,identifying the current encoded block at 810 may include receiving acompressed bitstream, such as the compressed bitstream 502 shown in FIG.5 , and reading the current encoded block, or a portion thereof, fromthe compressed bitstream.

A decoded block may be generated at 820 by decoding the current encodedblock identified at 810. Decoding the current encoded block at 820 mayinclude generating a prediction block for the current blockcorresponding to the current encoded block, which may includeidentifying a prediction model at 822, identifying reference values at824, and generating prediction values at 826.

Identifying the prediction model at 822 may include identifying amachine learning prediction coding model, such as an artificial neuralnetwork prediction coding model, which may generate each predictionvalues from the prediction block based on available reference values.For example, identifying the prediction model at 822 may includereading, extracting, or decoding data, such as a prediction modelidentifier, indicating the prediction model for decoding the currentencoded block, from the compressed bitstream received at 810, such asfrom a header for the current encoded block. In an example, identifyingthe prediction model at 822 may include identifying a set of non-linearfunctions, such as high dimensional non-linear functions, representing,or generated by, a respective machine learning prediction coding model.

Available reference values may be identified at 824. For example, theprediction model identified at 822 may be an intra-prediction model andidentifying the available reference values at 824 may includeidentifying the prediction values based on available reference valuesthat correspond to pixels spatially proximate to, such as adjacent to orneighboring, the current block from the current frame, such as values ofpreviously predicted, encoded, decoded, and reconstructed pixels. Inanother example, the prediction model identified at 822 may be aninter-prediction model and identifying the available reference values at824 may include identifying the prediction values based on availablereference values, such as values of previously predicted, encoded,decoded, and reconstructed pixels, determined based on a reference fameother than the current frame. In another example, the prediction modelidentified at 822 may be a compound inter-inter-prediction model andidentifying the available reference values at 824 may include generatingthe prediction values based on available reference values, such asvalues of previously predicted, encoded, decoded, and reconstructedpixels, determined based on multiple reference frames other than thecurrent frame. In another example, the prediction model identified at822 may be a compound inter-intra-prediction model and identifying theavailable reference values at 824 may include generating the predictionvalues, such as values of previously predicted, encoded, decoded, andreconstructed pixels, based on available reference values determinedbased on the current frame and based on a reference fame other than thecurrent frame.

Prediction values may be generated at 826 based on the prediction modelidentified at 822 and the available reference values identified at 824.For example, generating prediction values at 826 based on the artificialneural network prediction coding model identified at 822 and theavailable reference values identified at 824 may include determining aprediction value for each pixel of the current block by inputting theavailable reference values identified at 824 to the artificial neuralnetwork prediction coding model identified at 822 and obtaining theoutput of the artificial neural network prediction coding modelsidentified at 822 as the prediction values. Encoding, or decoding, usingan artificial neural network prediction coding model may includeinstantiating or operating an instance of an artificial neural networkdescribed by the artificial neural network model.

Although not shown expressly in FIG. 8 , generating a decoded block bydecoding the current encoded block at 820 may include performing otherelements of video decoding, such as entropy decoding by an entropydecoding unit, such as the entropy decoding unit 510 shown in FIG. 5 ,dequantization by a dequantization unit, such as the dequantization unit520 shown in FIG. 5 , inverse transformation by an inverse transformunit, such as the inverse transform unit 530 shown in FIG. 5 ,reconstruction by a reconstruction unit, such as the reconstruction unit550 shown in FIG. 5 , or any other aspect of video coding.

The decoded block may be output at 830. For example, the decoded blockmay be included in a decoded frame, which may be output, such as shownat 850 in FIG. 8 .

Other implementations of decoding using machine learning predictioncoding models 800 are available. In some implementations, additionalelements of decoding using machine learning prediction coding models canbe added, certain elements can be combined, and/or certain elements canbe removed.

FIGS. 9-18 are diagrams of examples of ad-hoc intra-prediction models inaccordance with implementations of this disclosure. Ad-hoc predictionmodels, such as the ad-hoc intra-prediction models shown in FIGS. 9-18 ,may be expressly, such as manually, generated or defined, and may differfrom the machine learning prediction models, such as artificial neuralnetwork prediction models, described herein.

In FIGS. 9-18 a current block for intra-prediction is indicated using athick black boarder; current pixels, or corresponding pixel locations,of the current block are indicated using a white background; andavailable referenced pixels, or corresponding pixel locations, that areavailable for intra-prediction of the current block are indicated usinga stippled background. Although some pixel locations are shown asavailable reference pixels for intra-prediction of the current block inFIGS. 9-18 , the pixel locations shown as available reference pixels maybe unavailable for intra-prediction, and other pixel locations may beavailable or unavailable for intra-prediction of the current block,based, for example, on block size, adjacent block size, and block scanorder. For example, the available reference pixels are shown in FIGS.9-18 above the current block and to the left of the current block, whichcorresponds with predicting the current frame using a block raster scanorder. In other examples (not shown), a reverse block raster scan ordermay be used, and the reference pixels available for intra-prediction ofthe current block may be below the current block and to the right of thecurrent block.

For simplicity and clarity, rows of a current block may be indicatedusing respective row identifiers (i); columns of the current block maybe indicated using respective column identifiers (j); a spatial locationof a current pixel (P) in the current block may be may be indicatedusing coordinates, such as Cartesian coordinates, which may include acombination of a row (i) location and a column (j) location, and whichmay be expressed as P_(i,j); and a spatial location of an availablereference pixel (R) in the current frame may be may be indicated usingcoordinates, such as Cartesian coordinates, relative to the currentblock, which may include a combination of a relative row (i) locationand a relative column (j) location, and which may be expressed asR_(i,j).

For example, the top row of the current block may be expressed as i=0,the second from the top row of the current block may be expressed asi=1, the third from the top row of the current block may be expressed asi=2, the bottom row of the current block may be expressed as i=3, theleft column of the current block may be expressed as j=0, the secondfrom the left column of the current block may be expressed as j=1, thethird from the left column of the current block may be expressed as j=2,the right column of the current block may be expressed as j=3, the pixelin the top left corner of the current block may be referred to asP_(0,0), the pixel in the top right corner of the current block may bereferred to as P_(0,3), the pixel in the bottom left corner of thecurrent block may be referred to as P_(3,0), the pixel in the bottomright corner of the current block may be referred to as P_(3,3), theavailable reference pixel in the row above the current block (i=-1) andin the left column (j=0) of the current block may be referred to asR_(-1,0), the available reference pixel in the column to the left of thecurrent block (j=-1) and in the top row (i=0) of the current block maybe referred to as R_(0,-1), and the available reference pixel above andto the left of the current block may be referred to as R_(-1,-1).

Although the current block shown in FIGS. 9-18 is a 4×4 block forsimplicity, any size block may be used. For example, a 64×64 block, a64×32 block, a 32×64 block, a 32×32 block, a 32×16 block, a 16×32 block,a 16×16 block, a 16×8 block, an 8×16 block, an 8×8 block, an 8×4 block,or a 4×8 block, may be used.

FIG. 9 is a diagram of an example of an ad-hoc DC intra-prediction modelin accordance with implementations of this disclosure. The ad-hoc DCintra-prediction model may include generating a prediction block, orprediction pixels of a prediction block, for a current block 900 basedon available reference pixel values proximate to, such as adjacent to orneighboring, the current block 900, such as available reference pixelsfrom a column 910 immediately to the left of the current block 900, arow 920 immediately above the current block 900, or both.

For example, the ad-hoc DC intra-prediction model may include generatingprediction pixel values each pixel of the current block 900 as afunction, such as an average, of the available reference pixels 930,932, 934, 936 immediately above the current block 900 and the availablereference pixels 940, 942, 944, 946 immediately to the left of thecurrent block 900.

FIG. 10 is a diagram of an example of an ad-hoc TrueMotion (TM)intra-prediction model in accordance with implementations of thisdisclosure. The ad-hoc TrueMotion intra-prediction model may includegenerating a prediction block, or prediction pixels of a predictionblock, for a current block 1000 based on available reference pixelvalues proximate to, such as adjacent to or neighboring, the currentblock 1000, such as available reference pixels from a column 1010immediately to the left of the current block 1000, a row 1020immediately above the current block 1000, or both.

The ad-hoc TrueMotion intra-prediction model may include generating aprediction pixel value for a current pixel (P_(i,j)) of the currentblock 1000 as a function of an available reference pixel (R_(i,-1)) tothe left of the current block 1000 in the row (i) of the current pixel(P_(i,j)), an available reference pixel (R_(-1,j)) above the currentblock 1000 in the column (j) of the current pixel (P_(i,j)), and theavailable reference pixel 1030 above and to the left (R_(-1,-1)) of thecurrent block 1000, which may include generating the prediction pixelvalue for a current pixel P_(i,j) of the current block 1000 as adifference between a sum of the respective available reference pixel(R_(i,-) ₁) to the left of the current block 1000 in the row (i) of thecurrent pixel (P_(i,j)) and the respective available reference pixel(R_(-1,j)) above the current block 1000 in the column (j) of the currentpixel (P_(i,j)) and an available reference pixel 1030 above and to theleft (R_(-1,-1)) of the current block 1000, which may be expressed asP_(i,j) = (R_(i,-1) + R_(-1,j)) - R_(-1,-1)).

For example, the ad-hoc TrueMotion intra-prediction model may includegenerating a prediction pixel value for the pixel 1040 in the top rightcorner (P_(0,3)) of the current block 1000 by determining a resultsubtracting the value of the available reference pixel 1030 above and tothe left (R_(-1,-1)) of the current block 1000 from a sum of theavailable reference pixel 1050 in the row above (i=-1) the current block1000 and in the column of the current pixel 1040 (j=3) and the availablereference pixel 1060 in the column to the left (j=-1) of the currentblock 1000 and in the row of the current pixel 1040 (i=0), which may beexpressed as P_(0,3) = (R_(-1,3) + R_(0,-1)) - R_(-1,-1)).

In another example, the ad-hoc TrueMotion intra-prediction model mayinclude generating a prediction pixel value for the pixel 1070 in thebottom left corner (P_(3,0)) of the current block 1000 by determining aresult subtracting the value of the available reference pixel 1030 aboveand to the left (R_(-1,-1)) of the current block 1000 from a sum of theavailable reference pixel 1080 in the row above (i=-1) the current block1000 and in the column of the current pixel 1070 (j=0) and the availablereference pixel 1070 in the column to the left (j=-1) of the currentblock 1000 and in the row of the current pixel 1070 (i=3), which may beexpressed as P_(3,0) = (R₋ _(1,0) + R_(3,-1)) - R_(-1,-1)).

FIG. 11 is a diagram of an example of an ad-hoc verticalintra-prediction model in accordance with implementations of thisdisclosure. The ad-hoc vertical intra-prediction model may includegenerating a prediction block, or prediction pixels of a predictionblock, for a current block 1100 based on available reference pixelvalues vertically proximate to, such as adjacent to or neighboring, thecurrent block 1100, such as available reference pixels from a row 1110immediately above the current block 1100. For example, the ad-hocvertical intra-prediction model may include generating prediction pixelvalues for the pixels in a respective column of a current block 1100 bycopying the value of an available reference pixel above the currentblock in the corresponding column, as indicated by the broken linespointing vertically down through the current block 1100.

In an example, predicted values for the pixels in the left column 1120of the current block 1100 may be generated using the ad-hoc verticalintra-prediction model based on, such as by copying, the value of theavailable reference pixel 1122 in the row immediately above the currentblock 1100 and in the corresponding column. Predicted values for thepixels in the second from the left column 1130 of the current block 1100may be generated using the ad-hoc vertical intra-prediction model basedon, such as by copying, the value of the available reference pixel 1132in the row immediately above the current block 1100 and in thecorresponding column. Predicted values for the pixels in the second fromthe right column 1140 of the current block 1100 may be generated usingthe ad-hoc vertical intra-prediction model based on, such as by copying,the value of the available reference pixel 1142 in the row immediatelyabove the current block 1100 and in the corresponding column. Predictedvalues for the pixels in the right column 1150 of the current block 1100may be generated using the ad-hoc vertical intra-prediction model basedon, such as by copying, the value of the available reference pixel 1152in the row immediately above the current block 1100 and in thecorresponding column.

FIG. 12 is a diagram of an example of an ad-hoc horizontalintra-prediction model in accordance with implementations of thisdisclosure. The ad-hoc horizontal intra-prediction model may includegenerating a prediction block, or prediction pixels of a predictionblock, for a current block 1200 based on available reference pixelvalues horizontally proximate to, such as adjacent to or neighboring,the current block 1200, such as available reference pixels from a column1210 immediately to the left of the current block 1200. For example, thead-hoc horizontal intra-prediction model may include generatingprediction pixel values for the pixels in a respective row of a currentblock 1200 by copying the value of an available reference pixel to theleft of the current block in the corresponding row, as indicated by thebroken lines pointing horizontally across through the current block1200.

In an example, predicted values for the pixels in the top row 1220 ofthe current block 1200 may be generated using the ad-hoc horizontalintra-prediction model based on, such as by copying, the value of theavailable reference pixel 1222 in the column immediately to the left ofthe current block 1200 and in the corresponding row. Predicted valuesfor the pixels in the second from the top row 1230 of the current block1200 may be generated using the ad-hoc horizontal intra-prediction modelbased on, such as by copying, the value of the available reference pixel1232 in the column immediately to the left of the current block 1200 andin the corresponding row. Predicted values for the pixels in the secondfrom the bottom row 1240 of the current block 1200 may be generatedusing the ad-hoc horizontal intra-prediction model based on, such as bycopying, the value of the available reference pixel 1242 in the columnimmediately to the left of the current block 1200 and in thecorresponding row. Predicted values for the pixels in the bottom row1250 of the current block 1200 may be generated using the ad-hochorizontal intra-prediction model based on, such as by copying, thevalue of the available reference pixel 1252 in the column immediately tothe left of the current block 1200 and in the corresponding row.

FIG. 13 is a diagram of an example of an ad-hoc diagonal down-right, or135°, intra-prediction model in accordance with implementations of thisdisclosure. The ad-hoc diagonal down-right intra-prediction model mayinclude generating a prediction block, or prediction pixels of aprediction block, for a current block 1300 based on available referencepixel values diagonally, such as at an angle of 135° counter-clockwisefrom right as 0°, proximate to, such as adjacent to or neighboring, thecurrent block 1300, such as available reference pixels from a column1310 immediately to the left of the current block 1300, availablereference pixels from a row 1320 immediately above the current block1300, or a combination thereof.

The ad-hoc diagonal down-right intra-prediction model may includegenerating prediction pixel values for respective pixels of a currentblock 1300 based on available reference pixels diagonally above and tothe left of the respective pixel location in the current block along, orproximate to, the corresponding diagonal, as indicated by the brokenlines pointing diagonally down and right at an angle of 135°counter-clockwise from right as 0° through the current block 1300.

For example, a predicted value for the pixel 1330 at the top left cornerof the current block 1300 may be generated using the ad-hoc diagonaldown-right intra-prediction model, such as using a 3-tap interpolationfilter centered on the available reference pixel 1332 along the 135°angle, which may include using the respective values of the availablereference pixel 1332 along the 135° angle and the available referencepixels 1334, 1336 adjacent to the available reference pixel 1332 alongthe 135° angle, such as by using a sum of one fourth of the value of theavailable reference pixel 1334 adjacent below the available referencepixel 1332 along the 135° angle, one fourth of the value of theavailable reference pixel 1336 adjacent to the right of the availablereference pixel 1332 along the 135° angle, and one half the value of theavailable reference pixel 1332 along the 135° angle.

A predicted value for the pixel 1340 in the top row and the second fromthe left column of the current block 1300 may be generated using thead-hoc diagonal down-right intra-prediction model, such as using a 3-tapinterpolation filter centered on the available reference pixel 1336along the 135° angle, which may include using the value of the availablereference pixel 1336 along the 135° angle and the respective values ofthe available reference pixels 1332, 1342 adjacent to the availablereference pixel 1336 along the 135° angle, such as by using a sum of onefourth of the value of the available reference pixel 1332 adjacent tothe left of the available reference pixel 1336 along the 135° angle, onefourth of the value of the available reference pixel 1342 adjacent tothe right of the available reference pixel 1336 along the 135° angle,and one half the value of the available reference pixel 1336 along the135° angle. Predicted values for the other pixels in the top row of thecurrent block 1300 may be similarly predicted using a 3-tapinterpolation filter centered on the respective available referencepixel along the respective 135° angle.

A predicted value for the pixel 1350 in the second from the top row andthe left column of the current block 1300 may be generated using thead-hoc diagonal down-right intra-prediction model, such as using a 3-tapinterpolation filter centered on the available reference pixel 1334along the 135° angle, which may include using the value of the availablereference pixel 1334 along the 135° angle and the respective values ofthe available reference pixels 1332, 1352 adjacent to the availablereference pixel 1334 along the 135° angle, such as by using a sum of onefourth of the value of the available reference pixel 1332 adjacent abovethe available reference pixel 1334 along the 135° angle, one fourth ofthe value of the available reference pixel 1352 adjacent below theavailable reference pixel 1334 along the 135° angle, and one half thevalue of the available reference pixel 1334 along the 135° angle.Predicted values for the other pixels in the left column of the currentblock 1300 may be similarly predicted using a 3-tap interpolation filtercentered on the respective available reference pixel along therespective 135° angle.

A predicted value for the pixel 1360 in the second from the top row andthe second from the left column of the current block 1300 may begenerated using the ad-hoc diagonal down-right intra-prediction model,such as by using the value of the immediately adjacent prediction pixel1330 along the 135° angle. Predicted values for the other pixels in thecurrent block 1300 may be similarly predicted using the value of theimmediately adjacent prediction pixel along the 135° angle.

FIG. 14 is a diagram of an example of an ad-hoc diagonal down-left, or45°, intra-prediction model in accordance with implementations of thisdisclosure. The ad-hoc diagonal down-left intra-prediction model mayinclude generating a prediction block, or prediction pixels of aprediction block, for a current block 1400 based on available referencepixel values diagonally, such as at an angle of 45° counter-clockwisefrom right as 0°, proximate to, such as adjacent to or neighboring, thecurrent block 1400, such as available reference pixels from a row 1410immediately above the current block 1400.

The ad-hoc diagonal down-left intra-prediction model may includegenerating prediction pixel values for respective pixels of a currentblock 1400 based on available reference pixels diagonally above and tothe right of the respective pixel location in the current block 1400along, or proximate to, the corresponding diagonal, as indicated by thebroken lines pointing diagonally down and left at an angle of 45°counter-clockwise from right as 0° through the current block 1400.

For example, a predicted value for the pixel 1420 at the bottom rightcorner of the current block 1400 may be generated using the ad-hocdiagonal down-left intra-prediction model, such as using a 3-tapinterpolation filter centered on the available reference pixel 1422along the 45° angle, which may include using the value of the availablereference pixel 1422 along the 45° angle and the respective values ofthe available reference pixels 1424, 1426 adjacent to the availablereference pixel 1422 along the 45° angle, such as by using a sum of onefourth of the value of the available reference pixel 1424 adjacent tothe left of the available reference pixel 1422 along the 45° angle, onefourth of the value of the available reference pixel 1426 adjacent tothe right of the available reference pixel 1422 along the 45° angle, andone half the value of the available reference pixel 1422 along the 45°angle. The available reference pixel 1426 adjacent to the right of theavailable reference pixel 1422 along the 45° angle is shown using abroken line boarder to indicate that a spatially corresponding referencepixel may be unavailable and the value of the available reference pixel1422 along the 45° angle may be used as the value of the reference pixel1426 adjacent to the right of the available reference pixel 1422 alongthe 45° angle.

A predicted value for the pixel 1430 in the top right corner of thecurrent block 1400 may be generated using the ad-hoc diagonal down-leftintra-prediction model, such as using a 3-tap interpolation filtercentered on the available reference pixel 1432 along the 45° angle,which may include using the value of the available reference pixel 1432along the 45° angle and the respective values of the available referencepixels 1434, 1436 adjacent to the available reference pixel 1432 alongthe 45° angle, such as by using a sum of one fourth of the value of theavailable reference pixel 1434 adjacent to the left of the availablereference pixel 1432 along the 45° angle, one fourth of the value of theavailable reference pixel 1436 adjacent to the right of the availablereference pixel 1432 along the 45° angle, and one half the value of theavailable reference pixel 1432 along the 45° angle. Predicted values forthe other pixels in the current block 1400 may be similarly predictedusing a 3-tap interpolation filter centered on the respective availablereference pixel along the respective 45° angle.

FIG. 15 is a diagram of an example of an ad-hoc horizontal-down-right,or 153°, intra-prediction model in accordance with implementations ofthis disclosure. Horizontal-down-right intra-prediction may includegenerating a prediction block, or prediction pixels of a predictionblock, for a current block 1500 based on available reference pixelvalues diagonally, such as at an angle of 153° counter-clockwise fromright as 0°, proximate to, such as adjacent to or neighboring, thecurrent block 1500, such as available reference pixels from a column1510 immediately to the left of the current block 1500, availablereference pixels from a row 1520 immediately above the current block1500, or a combination thereof.

Horizontal-down-right intra-prediction may include generating predictionpixel values for respective pixels of a current block 1500 based onavailable reference pixels diagonally above and to the left of thecurrent block 1500 along, or proximate to, the corresponding diagonal,as indicated by the broken lines pointing diagonally down and right atan angle of 153° counter-clockwise from right as 0° through the currentblock 1500.

For example, a predicted value for the pixel 1530 at the top left cornerof the current block 1500 may be generated using horizontal-down-rightintra-prediction, such as using a 2-tap interpolation filter centeredalong the 153° angle, which may be between the available referencepixels 1532, 1534 proximate to the 153° angle, and which may includeusing the respective values of the available reference pixels 1532, 1534proximate to the 153° angle. Predicted values for the other pixels inthe left column of the current block 1500 may be similarly predictedusing a 2-tap interpolation filter centered along the respective 153°angle, which may include using the respective two available referencepixels proximate to the 153° angle.

A predicted value for the pixel 1540 in the top row and the second fromthe left column the current block 1500 may be generated usinghorizontal-down-right intra-prediction, such as using a 3-tapinterpolation filter centered on the available reference pixel 1532along, or most proximate to, the 153° angle, which may include using thevalue of the available reference pixel 1532 along, or most proximate to,the 153° angle and the respective values of the available referencepixels 1534, 1542 adjacent to the available reference pixel 1532 along,or most proximate to, the 153° angle. A predicted value for the pixel1550 in the second row from the top and the second column from the leftof the current block 1500 may be generated using horizontal-down-rightintra-prediction, such as using a 3-tap interpolation filter centered onthe available reference pixel 1534 along, or most proximate to, the 153°angle, which may include using the value of the available referencepixel 1534 along, or most proximate to, the 153° angle and therespective values of the available reference pixels 1532, 1552 adjacentto the available reference pixel 1534 along, or most proximate to, the153° angle. A predicted value for the pixel 1560 in the third row fromthe top and the second column from the left of the current block 1500may be generated using horizontal-down-right intra-prediction, such asusing a 3-tap interpolation filter centered on the available referencepixel 1552 along, or most proximate to, the 153° angle, which mayinclude using the value of the available reference pixel 1552 along, ormost proximate to, the 153° angle and the respective values of theavailable reference pixels 1534, 1562 adjacent to the availablereference pixel 1552 along, or most proximate to, the 153° angle.Predicted values for the other pixels in the second column from the leftof the current block 1500 may be similarly predicted using a 3-tapinterpolation filter centered on the respective available referencepixel along the respective 153° angle.

A predicted value for the pixel 1570 in the top row and the third columnfrom the left of the current block 1500 may be generated usinghorizontal-down-right intra-prediction, such as using a 3-tapinterpolation filter centered on the available reference pixel 1542along, or most proximate to, the 153° angle, which may include using thevalue of the available reference pixel 1542 along, or most proximate to,the 153° angle and the respective values of the available referencepixels 1532, 1544 adjacent to the available reference pixel 1542 along,or most proximate to, the 153° angle. Predicted values for the otherpixels in the top row of the current block 1500 may be similarlypredicted using a 3-tap interpolation filter centered on the respectiveavailable reference pixel along the respective 153° angle.

A predicted value for the pixel 1580 in the second from the top row andthe third from the left column of the current block 1500 may begenerated using horizontal-down-right intra-prediction, such as by usingthe value of the prediction pixel 1530 along, or most proximate to, the153° angle, such as the prediction pixel 1530 in the row above the pixel1580 in the second from the top row and the third from the left columnof the current block 1500 and in the column two columns to the left ofthe pixel 1580 in the second from the top row and the third from theleft column of the current block 1500. Predicted values for the otherpixels in the current block 1500 may be similarly predicted using thevalue of the prediction pixel along, or most proximate to, the 153°angle, such as the prediction pixel in the row above the respectivecurrent pixel and in the column two columns to the left of therespective current pixel.

FIG. 16 is a diagram of an example of an ad-hoc vertical-down-right, or117°, intra-prediction model in accordance with implementations of thisdisclosure. Vertical-down-right intra-prediction may include generatinga prediction block, or prediction pixels of a prediction block, for acurrent block 1600 based on available reference pixel values diagonally,such as at an angle of 117° counter-clockwise from right as 0°,proximate to, such as adjacent to or neighboring, the current block1600, such as available reference pixels from a column 1610 immediatelyto the left of the current block 1600, available reference pixels from arow 1620 immediately above the current block 1600, or a combinationthereof.

Vertical-down-right intra-prediction may include generating predictionpixel values for respective pixels of a current block 1600 based onavailable reference pixels diagonally above and to the left of thecurrent block 1600 along, or proximate to, the corresponding diagonal,as indicated by the broken lines pointing diagonally down and right atan angle of 117° counter-clockwise from right as 0° through the currentblock 1600.

For example, a predicted value for the pixel 1630 at the top left cornerof the current block 1600 may be generated using vertical-down-rightintra-prediction, such as using a 2-tap interpolation filter centeredalong the 117° angle, which may be between the available referencepixels 1632, 1634 proximate to the 117° angle, and which may includeusing the respective values of the available reference pixels 1632, 1634proximate to the 117° angle. Predicted values for the other pixels inthe top row of the current block 1600 may be similarly predicted using a2-tap interpolation filter centered along the respective 117° angle,which may include using the respective two available reference pixelsproximate to the corresponding 117° angle.

A predicted value for the pixel 1640 in the second row from the top andthe left column of the current block 1600 may be generated usingvertical-down-right intra-prediction, such as using a 3-tapinterpolation filter centered on the available reference pixel 1632along, or most proximate to, the 117° angle, which may include using thevalue of the available reference pixel 1632 along, or most proximate to,the 117° angle and the respective values of the available referencepixels 1634, 1642 adjacent to the available reference pixel 1632 along,or most proximate to, the 117° angle. A predicted value for the pixel1650 in the second row from the top and the second column from the leftof the current block 1600 may be generated using vertical-down-rightintra-prediction, such as using a 3-tap interpolation filter centered onthe available reference pixel 1634 along, or most proximate to, the 117°angle, which may include using the value of the available referencepixel 1632 along, or most proximate to, the 117° angle and therespective values of the available reference pixels 1632, 1652 adjacentto the available reference pixel 1634 along, or most proximate to, the117° angle. Predicted values for the other pixels in the second row fromthe top of the current block 1600 may be similarly predicted using a3-tap interpolation filter centered on the respective availablereference pixel along, or most proximate to, the respective 117° angle.

A predicted value for the pixel 1660 in the third row from the top andthe left column of the current block 1600 may be generated usingvertical-down-right intra-prediction, such as using a 3-tapinterpolation filter centered on the available reference pixel 1642along, or most proximate to, the 117° angle, which may include using thevalue of the available reference pixel 1642 along, or most proximate to,the 117° angle and the respective values of the available referencepixels 1632, 1662 adjacent to the available reference pixel 1642 along,or most proximate to, the 117° angle.

A predicted value for the pixel 1670 in the fourth row from the top andthe left column of the current block 1600 may be generated usingvertical-down-right intra-prediction, such as using a 3-tapinterpolation filter centered on the available reference pixel 1662along, or most proximate to, the 117° angle, which may include using thevalue of the available reference pixel 1662 along, or most proximate to,the 117° angle and the respective values of the available referencepixels 1642, 1672 adjacent to the available reference pixel 1662 along,or most proximate to, the 117° angle. Predicted values for the otherpixels in the left column of the current block 1600 may be similarlypredicted using a 3-tap interpolation filter centered on the respectiveavailable reference pixel along, or most proximate to, the respective117° angle.

A predicted value for the pixel 1680 in the third from the top row andthe second from the left column of the current block 1600 may begenerated using vertical-down-right intra-prediction, such as by usingthe value of the prediction pixel 1630 along, or most proximate to, the117° angle, such as the prediction pixel 1630 in the row two rows abovethe pixel 1680 in the third from the top row and the second from theleft column of the current block 1600 and in the column to the left ofthe pixel 1680 in the third from the top row and the second from theleft column of the current block 1600. Predicted values for the otherpixels in the current block 1600 may be similarly predicted using thevalue of the prediction pixel along, or most proximate to, the 117°angle, such as the prediction pixel in the row two rows above therespective current pixel and in the column to the left of the respectivecurrent pixel.

FIG. 17 is a diagram of an example of an ad-hoc vertical-down-left, or63°, intra-prediction model in accordance with implementations of thisdisclosure. Vertical-down-left intra-prediction may include generating aprediction block, or prediction pixels of a prediction block, for acurrent block 1700 based on available reference pixel values diagonally,such as at an angle of 63° counter-clockwise from right as 0°, proximateto, such as adjacent to or neighboring, the current block 1700, such asavailable reference pixels from a row 1710 immediately above the currentblock 1700, or a combination thereof.

Vertical-down-left intra-prediction may include generating predictionpixel values for respective pixels of a current block 1700 based onavailable reference pixels diagonally above and to the right of thecurrent block 1700 along, or proximate to, the corresponding diagonal,as indicated by the broken lines pointing diagonally down and left at anangle of 63° counter-clockwise from right as 0° through the currentblock 1700.

For example, a predicted value for the pixel 1720 at the top left cornerof the current block 1700 may be generated using vertical-down-leftintra-prediction, such as using a 3-tap interpolation filter centered onthe available reference pixel 1722 along, or most proximate to, the 63°angle, which may include using the value of the available referencepixel 1722 along, or most proximate to, the 63° angle and the respectivevalues of the available reference pixels 1724, 1726 adjacent to theavailable reference pixel 1732 along the 63° angle. Predicted values forthe other pixels in the top row of the current block 1700 may besimilarly predicted using a 3-tap interpolation filter centered on theavailable reference pixel along, or most proximate to, the 63° angle,which may include using the value of the available reference pixelalong, or most proximate to, the 63° angle and the respective values ofthe available reference pixels adjacent to the available reference pixelalong, or most proximate to, the 63° angle.

A predicted value for the pixel 1730 in the second from the top row andthe left column of the current block 1700 may be generated usingvertical-down-left intra-prediction, such as using a 2-tap interpolationfilter centered along the 63° angle, which may be between the availablereference pixels 1722, 1724 proximate to the 63° angle, and which mayinclude using the respective values of the available reference pixels1722, 1724 proximate to the 63° angle. Predicted values for the otherpixels in the second from the top row of the current block 1700 may besimilarly predicted using the 2-tap interpolation filter centered alongthe respective 63° angle, which may include using the respective twoavailable reference pixels proximate to the 63° angle.

Predicted values for the other pixels immediately below pixels predictedusing a 4-tap interpolation filter may be similarly predicted using the2-tap interpolation filter centered along the respective 63° angle,which may include using the respective two available reference pixelsproximate to the 63° angle. Predicted values for the other pixelsimmediately below pixels predicted using a 2-tap interpolation filtermay be similarly predicted using the 3-tap interpolation filter centeredon the available reference pixel along, or most proximate to, the 63°angle, which may include using the value of the available referencepixel along, or most proximate to, the 63° angle and the respectivevalues of the available reference pixels adjacent to the availablereference pixel along, or most proximate to, the 63° angle.

FIG. 18 is a diagram of an example of an ad-hoc horizontal-up-right, or207°, intra-prediction model in accordance with implementations of thisdisclosure. Horizontal-up-right intra-prediction may include generatinga prediction block, or prediction pixels of a prediction block, for acurrent block 1800 based on available reference pixel values diagonally,such as at an angle of 207° counter-clockwise from right as 0°,proximate to, such as adjacent to or neighboring, the current block1800, such as available reference pixels from a column 1810 immediatelyto the left of the current block 1800, or a combination thereof.

For example, horizontal-up-right intra-prediction may include generatingprediction pixel values for respective pixels of a current block 1800based on available reference pixels diagonally below and to the left ofthe current block 1800 along, or proximate to, the correspondingdiagonal, as indicated by the broken lines pointing diagonally up andright at an angle of 207° counter-clockwise from right as 0° through thecurrent block 1800.

For example, a predicted value for the pixel 1820 at the bottom leftcorner of the current block 1800 may be generated usinghorizontal-up-right intra-prediction based on, such as by copying, thevalue of the available reference pixel 1822 in the column immediately tothe left of the current block 1800 and in the corresponding row. In someimplementations, reference pixels below the available reference pixel1822 in the column immediately to the left of the current block 1800 andin the corresponding row may be unavailable. Predicted values for theother pixels in the bottom row of the current block 1800 may besimilarly predicted, such as by copying the available reference pixel1822 in the column immediately to the left of the current block 1800 andin the corresponding row.

A predicted value for the pixel 1830 at the top left corner of thecurrent block 1800 may be generated using horizontal-up-rightintra-prediction, such as using a 2-tap interpolation filter centeredalong the 207° angle, which may be between the available referencepixels 1832, 1834 proximate to the 207° angle, and which may includeusing the respective values of the available reference pixels 1832, 1834proximate to the 207° angle. Predicted values for the other pixels inthe left column of the current block 1800 may be similarly predictedusing a 2-tap interpolation filter centered along the respective 207°angle, which may include using the respective two available referencepixels proximate to the 207° angle. A predicted value for the pixel 1840in the second row from the bottom and the second column from the left ofthe current block 1800 may be generated using horizontal-up-rightintra-prediction, such as using a 3-tap interpolation filter centered onthe available reference pixel 1822 along, or most proximate to, the 207°angle, which may include using the value of the available referencepixel 1822 along, or most proximate to, the 207° angle and therespective values of the available reference pixel 1842 adjacent to theavailable reference pixel 1822 along, or most proximate to, the 207°angle.

A predicted value for the pixel 1850 in the top row and the second fromthe left column the current block 1800 may be generated usinghorizontal-up-right intra-prediction, such as using a 3-tapinterpolation filter centered on the available reference pixel 1834along, or most proximate to, the 207° angle, which may include using thevalue of the available reference pixel 1834 along, or most proximate to,the 207° angle and the respective values of the available referencepixels 1832, 1842 adjacent to the available reference pixel 1834 along,or most proximate to, the 207° angle. Predicted values for the otherpixels in the second column from the left of the current block 1800 maybe similarly predicted using a 3-tap interpolation filter centered onthe respective available reference pixel along the respective 207°angle. Predicted values for the other pixels in the current block 1800may be similarly predicted using the value of the prediction pixelalong, or most proximate to, the 207° angle, such as the predictionpixel in the row below the respective current pixel and in the columntwo columns to the left of the respective current pixel.

FIG. 19 is a flowchart diagram of an example of generating artificialneural network models 1900 in accordance with implementations of thisdisclosure. Generating artificial neural network models 1900 may beimplemented in an encoder, such as the encoder 400 shown in FIG. 4 . Forexample, the intra/inter prediction unit 410 of the encoder 400 shown inFIG. 4 , may generating artificial neural network models 1900.

Generating artificial neural network models 1900 includes obtaining aset of coding modes at 1910, obtaining coding models at 1920, obtainingtraining data at 1930, obtaining a classifier model at 1940,partitioning the training data at 1950, obtaining trained coding modelsat 1960, and outputting the trained artificial neural network codingmodels at 1970.

A set of encoding modes may be obtained at 1910. Obtaining the encodingmodes may include identifying an encoding mode type, such asintra-prediction coding, inter-prediction coding, compound inter-interprediction coding, or compound inter-intra prediction coding. Obtainingthe encoding modes may include identifying a cardinality (M) of the setof encoding modes for the identified encoding mode type. For example,the set of intra-prediction modes may have a defined cardinality (M),such as 64 (M=64). Obtaining the encoding modes may include identifyingmay include obtaining a set of quality levels (Q), such as two encodingquality levels (Q=2). Obtaining the encoding modes may include obtaininga set of coding model classes. Each coding model class may be associatedwith a respective encoding mode and a respective encoding quality level.

Coding models may be obtained at 1920. Obtaining the coding models mayinclude obtaining untrained, or partially trained, coding models, suchas machine learning or artificial intelligence coding models, for thecoding model classes. A respective coding model may be obtained for eachcoding model class. The coding models may be artificial neural networkmodels. In some embodiments, aspects of the untrained coding models,such as the artificial neural network architecture, may be specifiedwith reference to the ad-hoc prediction models corresponding to therespective class.

Training data may be obtained at 1940. The training data may includemultiple training sets, such as two million training sets, or any othernumber of training sets. A training set {x, z}, or vector, may include acurrent block (z) for encoding, such as a 4×4 block, and referencevalues (x), such as nine reference values. The current block (z) maycorrespond with an input block, such as an input block from an inputframe. In some embodiments, the reference values (x) may be input pixelvalues, such as input pixel values proximate to, such as adjacent to orneighboring, the current block (z). For example, the reference values(x) may include input pixel values from a column immediately adjacent tothe left of the current block (z), input pixel values from a rowimmediately adjacent above the current block (z), and an input pixelvalue from immediately adjacent above and to the left of the currentblock (z). In some embodiments, the reference values (x) may bepreviously encoded and reconstructed proximate to, such as adjacent toor neighboring, the current block (z). Other reference values, such asreference values below and to the left of the current block (z),reference values above and to the right of the current block (z),reference values from locations in a current input, or reconstruction,frame that are not immediately adjacent to the current block (z), may beused. In some embodiments, the training data, or a portion thereof, suchas the reference vales (x) may be generated by encoding and decoding theinput frames. For example, obtaining the training data may includeobtaining an input portion, such as one or more input frames, of thetraining data, and generating a reconstructed portion of the trainingdata by encoding the input frames to generate encoded frames, anddecoding the encoded frames to generate reconstructed data on a percoding model class (MxQ) basis.

A classifier model may be obtained at 1930. The classifier model mayexpress one or more defined rules or functions that may be used toclassify an input, or training, vector {x, z} into a coding model classfrom the set of coding model classes. For example, the classifier modelmay classify the training data by encoding one or more input frames fromthe training data. In an example, the classifier model may classify thetraining data by encoding one or more input frames from the trainingdata using ad-hoc encoding models, such as the ad-hoc encoding modelsshown in FIGS. 9-18 , to identify an optimal coding model class for eachtraining set {x, z}, such as based on an optimization metric, such asSAD or SSE, which may be similar to the prediction coding described withrespect to FIG. 6 , except as described herein or otherwise clear fromcontext. In some embodiments, the classifier model may classify thetraining data by encoding one or more input frames from the trainingdata using previously generated artificial neural network encodingmodels. In some embodiments, the classifier model may be independentfrom the encoding models. For example, the classifier model may classifythe input sets {x, z} based on the input frame data, such as based onedge directionality and strength detected based on the input frame datafor a respective input block (z).

The training data identified at 1940 may be classified, or partitioned,at 1950, such as using the classifier model obtained at 1930, such thateach training set {x, z} is classified into a respective class,corresponding to a respective prediction mode from the prediction modesidentified at 1910, which may include generating M subsets of thetraining data, each subset of the training data corresponding to arespective class. In some embodiments, partitioning the training datamay include generating MQ subsets of the training data, each subsetcorresponding to a combination of a prediction mode M and a qualitylevel Q.

Trained coding models may be obtained at 1960, which may includetraining the coding models obtained at 1920 based on the training dataobtained at 1940 and partitioned at 1950, such as using neural networkregression. Each coding model may be trained using the training datafrom the subset, or partition, of training data associated with therespective coding model class.

Training a coding model may include identifying a training set {x, z}from the training data corresponding to a respective class, inputtingthe reference values (x) from the training set {x, z} into theartificial neural network coding model, obtaining a prediction block(z_(p)) output by the artificial neural network coding model in responseto the input reference values (x), determining an accuracy metric, suchas a sum of absolute differences (SAD) or a sum of square errors (SSE),indicating a difference between the prediction block (z_(p)) and theinput block (z) from the training set {x, z}, and automatically updatingone or more weights, thresholds, or other configurable parametersdescribed or defined by the artificial neural network model to improvethe accuracy of the artificial neural network. Training a coding modelmay include iteratively training the coding model based on each trainingset {x, z} from the training data corresponding to a respective class.

Obtaining the trained coding models at 1960 may include determiningwhether defined convergence criteria are satisfied, and, iterativelyrepeating obtaining training data at 1930, obtaining the classifiermodel at 1940, partitioning the training data at 1950, and obtainingtrained coding models at 1960, such that the defined convergencecriteria are satisfied, as indicated by the broken directional line at1965.

Obtaining the training data at 1930 subsequent to determining whetherdefined convergence criteria are satisfied at 1965 may includeregenerating the training data by encoding the input training data basedon the partially trained coding models obtained at 1960 in the previousiteration. In some implementations, obtaining the training data at 1930subsequent to determining whether defined convergence criteria aresatisfied at 1965 may be omitted and each training iteration may includepartitioning the training data at 1950 and obtaining trained codingmodels at 1960 based on the training data identified in accordance withthe first iteration. In some embodiments, obtaining the training data at1930 may include obtaining different training data, or partiallydifferent training data for each iteration.

In some embodiments, obtaining the classifier model at 1940 may includeobtaining a first classifier model for a first training iteration, suchas a first classifier model based on the ad-hoc prediction models or afirst classifier model based on the input data, and obtaining a secondclassifier model based on the partially trained artificial neuralnetwork models for iterations subsequent to the first iteration.

In some embodiments, the defined convergence criteria may include aninter-iteration minimum classification variance metric. For example, theinter-iteration minimum classification variance metric may indicate aminimum cardinality of differences in the partitioned training datasubsets. For example, the cardinality of training set classificationsfor a current iteration that differs from the training setclassifications from an immediately preceding iteration may be below theinter-iteration minimum classification variance metric and theartificial neural network models trained in accordance with the currentiteration may be identified as the trained artificial neural networkmodels, which may be output, such as stored, at 1970. Outputting thetrained artificial neural network models at 1970 may include outputting,such as storing, the artificial neural network model architecture andparameters, such as weights, for each model. In an example, outputtingthe trained artificial neural network models at 1970 may includeoutputting, such as storing, respective sets of non-linear functions,such as high dimensional non-linear functions, representing, orgenerated by, the corresponding machine learning prediction codingmodel.

As used herein, the terms “optimal”, “optimized”, “optimization”, orother forms thereof, are relative to a respective context and are notindicative of absolute theoretic optimization unless expressly specifiedherein.

The words “example” or “exemplary” are used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “example” or “exemplary” not necessarily to be construed aspreferred or advantageous over other aspects or designs. Rather, use ofthe words “example” or “exemplary” is intended to present concepts in aconcrete fashion. As used in this application, the term “or” is intendedto mean an inclusive “or” rather than an exclusive “or”. That is, unlessspecified otherwise, or clear from context, “X includes A or B” isintended to mean any of the natural inclusive permutations. That is, ifX includes A; X includes B; or X includes both A and B, then “X includesA or B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Moreover, use of the term “an embodiment” or “one embodiment” or“an implementation” or “one implementation” throughout is not intendedto mean the same embodiment or implementation unless described as such.As used herein, the terms “determine” and “identify”, or any variationsthereof, includes selecting, ascertaining, computing, looking up,receiving, determining, establishing, obtaining, or otherwiseidentifying or determining in any manner whatsoever using one or more ofthe devices shown in FIG. 1 .

Further, for simplicity of explanation, although the figures anddescriptions herein may include sequences or series of steps or stages,elements of the methods disclosed herein can occur in various ordersand/or concurrently. Additionally, elements of the methods disclosedherein may occur with other elements not explicitly presented anddescribed herein. Furthermore, one or more elements of the methodsdescribed herein may be omitted from implementations of methods inaccordance with the disclosed subject matter.

The implementations of the transmitting computing and communicationdevice 100A and/or the receiving computing and communication device 100B(and the algorithms, methods, instructions, etc. stored thereon and/orexecuted thereby) can be realized in hardware, software, or anycombination thereof. The hardware can include, for example, computers,intellectual property (IP) cores, application-specific integratedcircuits (ASICs), programmable logic arrays, optical processors,programmable logic controllers, microcode, microcontrollers, servers,microprocessors, digital signal processors or any other suitablecircuit. In the claims, the term “processor” should be understood asencompassing any of the foregoing hardware, either singly or incombination. The terms “signal” and “data” are used interchangeably.Further, portions of the transmitting computing and communication device100A and the receiving computing and communication device 100B do notnecessarily have to be implemented in the same manner.

Further, in one implementation, for example, the transmitting computingand communication device 100A or the receiving computing andcommunication device 100B can be implemented using a computer programthat, when executed, carries out any of the respective methods,algorithms and/or instructions described herein. In addition oralternatively, for example, a special purpose computer/processor can beutilized which can contain specialized hardware for carrying out any ofthe methods, algorithms, or instructions described herein.

The transmitting computing and communication device 100A and receivingcomputing and communication device 100B can, for example, be implementedon computers in a real-time video system. Alternatively, thetransmitting computing and communication device 100A can be implementedon a server and the receiving computing and communication device 100Bcan be implemented on a device separate from the server, such as ahand-held communications device. In this instance, the transmittingcomputing and communication device 100A can encode content using anencoder 400 into an encoded video signal and transmit the encoded videosignal to the communications device. In turn, the communications devicecan then decode the encoded video signal using a decoder 500.Alternatively, the communications device can decode content storedlocally on the communications device, for example, content that was nottransmitted by the transmitting computing and communication device 100A.Other suitable transmitting computing and communication device 100A andreceiving computing and communication device 100B implementation schemesare available. For example, the receiving computing and communicationdevice 100B can be a generally stationary personal computer rather thana portable communications device and/or a device including an encoder400 may also include a decoder 500.

Further, all or a portion of implementations can take the form of acomputer program product accessible from, for example, a tangiblecomputer-usable or computer-readable medium. A computer-usable orcomputer-readable medium can be any device that can, for example,tangibly contain, store, communicate, or transport the program for useby or in connection with any processor. The medium can be, for example,an electronic, magnetic, optical, electromagnetic, or a semiconductordevice. Other suitable mediums are also available.

The above-described implementations have been described in order toallow easy understanding of the application are not limiting. On thecontrary, the application covers various modifications and equivalentarrangements included within the scope of the appended claims, whichscope is to be accorded the broadest interpretation so as to encompassall such modifications and equivalent structure as is permitted underthe law.

What is claimed is:
 1. A non-transitory computer-readable storage mediumhaving stored thereon an encoded bitstream, wherein the encodedbitstream is configured for decoding by operations comprising:generating, by a processor, a decoded frame by decoding a current framefrom an encoded bitstream, wherein decoding includes: identifying acurrent encoded block from the current frame; identifying a predictioncoding model for the current block, wherein the prediction coding modelis a machine learning prediction coding model from a plurality ofmachine learning prediction coding models; identifying reference valuesfor decoding the current block based on the prediction coding model;obtaining prediction values based on the prediction coding model and thereference values; generating a decoded block corresponding to thecurrent encoded block based on the prediction values; and including thedecoded block in the decoded frame; and outputting a reconstructed framebased on the decoded frame.
 2. The non-transitory computer-readablestorage medium of claim 1, wherein: identifying the prediction codingmodel includes identifying a set of non-linear functions representingthe machine learning prediction coding model; and obtaining theprediction values includes obtaining the prediction values using atleast one non-linear function from the set of non-linear functions. 3.The non-transitory computer-readable storage medium of claim 1, whereindecoding includes: decoding a prediction coding model identifier formthe encoded bitstream, the prediction coding model identifier indicatingthe prediction coding model; and identifying the prediction coding modelbased on the prediction coding model identifier.
 4. The non-transitorycomputer-readable storage medium of claim 3, wherein decoding includes:identifying a quality level for decoding the current frame; andidentifying the prediction coding model based on the prediction codingmodel identifier and the quality level.
 5. The non-transitorycomputer-readable storage medium of claim 1, wherein identifying theprediction coding model includes identifying a prediction coding type,wherein the prediction coding type is an intra-prediction coding type,an inter-prediction coding type, or a compound prediction coding type.6. The non-transitory computer-readable storage medium of claim 1,wherein the prediction coding model is an intra-prediction coding modelfrom a plurality of intra-prediction coding models.
 7. Thenon-transitory computer-readable storage medium of claim 1, whereinobtaining the prediction values includes: using the reference values asinput values for an artificial neural network corresponding to theprediction coding model such that the prediction values are output bythe artificial neural network in response to the reference values. 8.The non-transitory computer-readable storage medium of claim 1, whereinidentifying the prediction coding model includes: identifying a trainedprediction coding model from a plurality of trained prediction codingmodels, trained by: identifying a prediction coding type, wherein theprediction coding type is an intra-prediction coding type, aninter-prediction coding type, or a compound prediction coding type;identifying a plurality of prediction coding modes associated with theprediction coding type, the plurality of prediction coding modes havinga defined cardinality; identifying a plurality of prediction codingmodels such that each prediction coding mode from the plurality ofprediction coding modes is associated with a respective predictioncoding model from the plurality of prediction coding models; andobtaining the plurality of trained prediction coding models by trainingthe prediction coding type using the plurality of prediction codingmodels as current prediction coding models; and identifying the trainedprediction coding model as the prediction coding model.
 9. Thenon-transitory computer-readable storage medium of claim 8, whereintraining the prediction coding type includes: obtaining partiallytrained prediction coding models by training the current predictioncoding models; determining whether a convergence criterion for theprediction coding type is satisfied based on the partially trainedprediction coding models; in response to a determination that theconvergence criterion for the prediction coding type is satisfied,identifying the partially trained prediction coding models as theplurality of trained prediction coding models; and in response to adetermination that the convergence criterion for the prediction codingtype is unsatisfied, training the prediction coding type using thepartially trained prediction coding models as the current predictioncoding models.
 10. The non-transitory computer-readable storage mediumof claim 9, wherein training the current prediction coding modelsincludes: obtaining training data, the training data including aplurality of training data sets, each training data set including arespective plurality of reference values and a respective block of inputpixel values; obtaining a classifier for partitioning the training data;obtaining a plurality of training data partitions by partitioning thetraining data using the classifier, wherein partitioning the trainingdata using the classifier includes obtaining a plurality of trainingdata partitions wherein each training data set from the plurality oftraining data sets is included in a respective training data partitionfrom the plurality of training data partitions in accordance with theclassifier, and wherein each training data partition from the pluralityof training data partitions is associated with a respective predictioncoding mode from the plurality of prediction coding modes; for eachprediction coding mode from the plurality of prediction coding modes:obtaining a set of internal parameter values for a current predictioncoding model from the current prediction coding models, the currentprediction coding model corresponding to the prediction coding mode; foreach training data set from the corresponding training data partition:obtaining prediction values output based on the current predictioncoding model in response to reference values from the current trainingdata set, wherein obtaining the prediction values includes using the setof internal parameter values as current internal parameter values;determining an accuracy metric based on a difference between theprediction values and input pixel values from the current training dataset; generating an updated set of internal parameter values based on thecurrent internal parameter values and the accuracy metric; andidentifying the updated set of internal parameter values as the set ofinternal parameter values.
 11. The non-transitory computer-readablestorage medium of claim 10, wherein: on a condition that the currentprediction coding models are untrained prediction coding models,obtaining the classifier includes identifying a first classifier as theclassifier; and on a condition that the current prediction coding modelsare partially trained prediction coding models, obtaining the classifierincludes identifying a second classifier as the classifier.
 12. Thenon-transitory computer-readable storage medium of claim 11, whereinidentifying the first classifier includes: identifying the firstclassifier such that the first classifier classifies a respectivetraining data set based on encoding the respective training data setusing ad-hoc prediction coding models that differ from the machinelearning prediction coding models.
 13. The non-transitorycomputer-readable storage medium of claim 12, wherein identifying thesecond classifier includes identifying the second classifier such thatthe second classifier classifies a respective training data set based onencoding the respective training data set using the partially trainedprediction coding models.
 14. The non-transitory computer-readablestorage medium of claim 10, wherein determining whether the convergencecriterion for the prediction coding type is satisfied includes: inresponse to a determination that the current prediction coding modelsare untrained prediction coding models, determining that the convergencecriterion for the prediction coding type is unsatisfied; in response toa determination that the current prediction coding models are partiallytrained prediction coding models: determining a cardinality ofdifferences between the plurality of training data partitions and apreviously generated plurality of training data partitions; in responseto a determination that the cardinality of differences is at least aminimum variance threshold, determining that the convergence criterionfor the prediction coding type is unsatisfied; and in response to adetermination that the minimum variance threshold exceeds thecardinality of differences, determining that the convergence criterionfor the prediction coding type is satisfied.
 15. A method comprising:generating, by a processor, an encoded frame by encoding a current framefrom an input video stream, wherein encoding includes: identifying acurrent block from the current frame; identifying a prediction codingmodel for the current block, wherein the prediction coding model is amachine learning prediction coding model from a plurality of machinelearning prediction coding models; identifying reference values for thecurrent block based on the prediction coding model; obtaining predictionvalues based on the prediction coding model and the reference values;generating an encoded block corresponding to the current encoded blockbased on the prediction values; and including the encoded block in theencoded frame; and including the encoded frame in an output bitstream.16. The method of claim 15, wherein encoding includes: including aprediction coding model identifier in the output bitstream, theprediction coding model identifier indicating the prediction codingmodel.
 17. The method of claim 15, wherein identifying the predictioncoding model includes: identifying a trained prediction coding modelfrom a plurality of trained prediction coding models, trained by:identifying a prediction coding type, wherein the prediction coding typeis an intra-prediction coding type, an inter-prediction coding type, ora compound prediction coding type; identifying a plurality of predictioncoding modes associated with the prediction coding type, the pluralityof prediction coding modes having a defined cardinality; identifying aplurality of prediction coding models such that each prediction codingmode from the plurality of prediction coding modes is associated with arespective prediction coding model from the plurality of predictioncoding models; and obtaining the plurality of trained prediction codingmodels by training the prediction coding type using the plurality ofprediction coding models as current prediction coding models; andidentifying the trained prediction coding model as the prediction codingmodel.
 18. The method of claim 17, wherein training the predictioncoding type includes: obtaining partially trained prediction codingmodels by training the current prediction coding models; determiningwhether a convergence criterion for the prediction coding type issatisfied based on the partially trained prediction coding models; inresponse to a determination that the convergence criterion for theprediction coding type is satisfied, identifying the partially trainedprediction coding models as the plurality of trained prediction codingmodels; and in response to a determination that the convergencecriterion for the prediction coding type is unsatisfied, training theprediction coding type using the partially trained prediction codingmodels as the current prediction coding models.
 19. The method of claim18, wherein training the current prediction coding models includes:obtaining training data, the training data including a plurality oftraining data sets, each training data set including a respectiveplurality of reference values and a respective block of input pixelvalues; obtaining a classifier for partitioning the training data;obtaining a plurality of training data partitions by partitioning thetraining data using the classifier, wherein partitioning the trainingdata using the classifier includes obtaining a plurality of trainingdata partitions wherein each training data set from the plurality oftraining data sets is included in a respective training data partitionfrom the plurality of training data partitions in accordance with theclassifier, and wherein each training data partition from the pluralityof training data partitions is associated with a respective predictioncoding mode from the plurality of prediction coding modes; for eachprediction coding mode from the plurality of prediction coding modes:obtaining a set of internal parameter values for a current predictioncoding model from the current prediction coding models, the currentprediction coding model corresponding to the prediction coding mode; foreach training data set from the corresponding training data partition:obtaining prediction values output based on the current predictioncoding model in response to reference values from the current trainingdata set, wherein obtaining the prediction values includes using the setof internal parameter values as current internal parameter values;determining an accuracy metric based on a difference between theprediction values and input pixel values from the current training dataset; generating an updated set of internal parameter values based on thecurrent internal parameter values and the accuracy metric; andidentifying the updated set of internal parameter values as the set ofinternal parameter values.
 20. An apparatus comprising: a memory; and aprocessor configured to: generate an encoded frame, wherein to generatethe encoded frame the processor executes instructions stored in thememory to encode a current frame from an input video stream, wherein toencode the current frame the processor executes the instructions to:identify a current encoded block from the current frame; identify aprediction coding model for the current block, wherein the predictioncoding model is a machine learning prediction coding model from aplurality of previously defined machine learning prediction codingmodels; identify reference values to encode the current block based onthe prediction coding model; obtain prediction values based on theprediction coding model and the reference values; generate an encodedblock corresponding to the current block based on the prediction values;and include the encoded block in the encoded frame; and include theencoded frame in an output bitstream.